library(sp)
## Warning: package 'sp' was built under R version 3.6.3
library(sf)
## Warning: package 'sf' was built under R version 3.6.3
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
library(ncdf4)
library(raster)
## Warning: package 'raster' was built under R version 3.6.3
library(rasterVis)
## Loading required package: terra
## Warning: package 'terra' was built under R version 3.6.3
## terra version 1.2.5
## Loading required package: lattice
## Warning: package 'lattice' was built under R version 3.6.3
## Loading required package: latticeExtra
## Warning: package 'latticeExtra' was built under R version 3.6.3
library(RColorBrewer)
library(maptools)
## Checking rgeos availability: TRUE
library(maps)
## Warning: package 'maps' was built under R version 3.6.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:terra':
##
## intersect, near, union
## The following objects are masked from 'package:raster':
##
## intersect, select, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.6.3
##
## Attaching package: 'tidyr'
## The following objects are masked from 'package:terra':
##
## expand, extract, pack, separate
## The following object is masked from 'package:raster':
##
## extract
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:latticeExtra':
##
## layer
library(ellipsis)
## Warning: package 'ellipsis' was built under R version 3.6.3
library(TSstudio)
## Warning: package 'TSstudio' was built under R version 3.6.3
library(astsa)
##
## Attaching package: 'astsa'
## The following object is masked from 'package:maps':
##
## unemp
# set path and filename
data_path <- "./data/"
folder_fuel <- "fuel/"
fuel_path <- paste(data_path, folder_fuel, sep="")
wind_filename <- "adaptor.mars.internal-1627157739.9280307-9233-3-bfb7e7ba-55aa-4a70-b917-3f52ac1fb472.nc"
wind_file <- paste(data_path, wind_filename, sep="")
wave_filename <- "adaptor.mars.internal-1627158731.6650586-13503-5-7b2a701a-5196-4d19-961f-f557a6c22ae4.nc"
wave_file <- paste(data_path, wave_filename, sep="")
freak_wave_filename <- "adaptor.mars.internal-1627159551.019192-19965-5-64e8f8ba-76ac-4f6b-88ed-f0f67c8e67c1.nc"
freak_wave_file <- paste(data_path, freak_wave_filename, sep="")
max_indiv_wave_height_filename <- "adaptor.mars.internal-1627160342.4202654-24930-3-c4e15954-a6b6-41c3-ad02-c96804c4d58f.nc"
max_indiv_wave_height_file <- paste(data_path, max_indiv_wave_height_filename, sep="")
Guardamos los rasterbrick (cubos) de las variables:
u_wind_comp_10 <- brick(wind_file,varname="u10")
v_wind_comp_10 <- brick(wind_file,varname="v10")
u_wind_comp_10
## class : RasterBrick
## dimensions : 361, 481, 173641, 300 (nrow, ncol, ncell, nlayers)
## resolution : 0.25, 0.25 (x, y)
## extent : -110.125, 10.125, -0.125, 90.125 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : D:/R/netcdf_M/data/adaptor.mars.internal-1627157739.9280307-9233-3-bfb7e7ba-55aa-4a70-b917-3f52ac1fb472.nc
## names : X1993.01.01, X1993.02.01, X1993.03.01, X1993.04.01, X1993.05.01, X1993.06.01, X1993.07.01, X1993.08.01, X1993.09.01, X1993.10.01, X1993.11.01, X1993.12.01, X1994.01.01, X1994.02.01, X1994.03.01, ...
## Date/time : 1993-01-01, 2017-12-01 (min, max)
## varname : u10
raster::plot(u_wind_comp_10$X1993.01.01)
raster::plot(v_wind_comp_10$X1993.01.01)
mean_wave_direc <- brick(wave_file,varname="mwd")
mean_wave_period <- brick(wave_file,varname="mwp")
signif_wave_height <- brick(wave_file,varname="swh")
mean_wave_direc
## class : RasterBrick
## dimensions : 181, 241, 43621, 300 (nrow, ncol, ncell, nlayers)
## resolution : 0.5, 0.5 (x, y)
## extent : -110.25, 10.25, -0.25, 90.25 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : D:/R/netcdf_M/data/adaptor.mars.internal-1627158731.6650586-13503-5-7b2a701a-5196-4d19-961f-f557a6c22ae4.nc
## names : X1993.01.01, X1993.02.01, X1993.03.01, X1993.04.01, X1993.05.01, X1993.06.01, X1993.07.01, X1993.08.01, X1993.09.01, X1993.10.01, X1993.11.01, X1993.12.01, X1994.01.01, X1994.02.01, X1994.03.01, ...
## Date/time : 1993-01-01, 2017-12-01 (min, max)
## varname : mwd
raster::plot(mean_wave_direc$X1993.01.01)
raster::plot(mean_wave_period$X1993.01.01)
raster::plot(signif_wave_height$X1993.01.01)
bfi_index <- brick(freak_wave_file,varname="bfi")
peak_wave_period <- brick(freak_wave_file,varname="pp1d")
wave_spectral_kurtosis <- brick(freak_wave_file,varname="wsk")
peak_wave_period
## class : RasterBrick
## dimensions : 181, 241, 43621, 300 (nrow, ncol, ncell, nlayers)
## resolution : 0.5, 0.5 (x, y)
## extent : -110.25, 10.25, -0.25, 90.25 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : D:/R/netcdf_M/data/adaptor.mars.internal-1627159551.019192-19965-5-64e8f8ba-76ac-4f6b-88ed-f0f67c8e67c1.nc
## names : X1993.01.01, X1993.02.01, X1993.03.01, X1993.04.01, X1993.05.01, X1993.06.01, X1993.07.01, X1993.08.01, X1993.09.01, X1993.10.01, X1993.11.01, X1993.12.01, X1994.01.01, X1994.02.01, X1994.03.01, ...
## Date/time : 1993-01-01, 2017-12-01 (min, max)
## varname : pp1d
raster::plot(bfi_index$X1993.01.01)
raster::plot(peak_wave_period$X1993.01.01)
raster::plot(wave_spectral_kurtosis$X1993.01.01)
max_individual_wave_height <- brick(max_indiv_wave_height_file)
max_individual_wave_height
## class : RasterBrick
## dimensions : 181, 241, 43621, 300 (nrow, ncol, ncell, nlayers)
## resolution : 0.5, 0.5 (x, y)
## extent : -110.25, 10.25, -0.25, 90.25 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : D:/R/netcdf_M/data/adaptor.mars.internal-1627160342.4202654-24930-3-c4e15954-a6b6-41c3-ad02-c96804c4d58f.nc
## names : X1993.01.01, X1993.02.01, X1993.03.01, X1993.04.01, X1993.05.01, X1993.06.01, X1993.07.01, X1993.08.01, X1993.09.01, X1993.10.01, X1993.11.01, X1993.12.01, X1994.01.01, X1994.02.01, X1994.03.01, ...
## Date/time : 1993-01-01, 2017-12-01 (min, max)
## varname : hmax
raster::plot(max_individual_wave_height$X1993.01.01)
Función de ayuda para dar formato largo:
files_fuel <- list.files(fuel_path)
getnc <- function(fl){
nc <- nc_open(fl)
dat <- ncvar_get(nc) %>% as.data.frame() %>%
mutate(speed = nc$dim$speed$vals) %>%
pivot_longer(cols = 1:12, names_to = "month", values_to = "fuel") %>%
mutate(month = factor(month, levels = c("V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12")))
nc_close(nc)
return(dat)}
Unimos todos los archivos por año de consumo de fuel:
alldata <- list()
years <- 1993:2017
for(i in 1:length(files_fuel)){
result <- getnc(paste(fuel_path,files_fuel[i],sep = ""))
result$year <- years[i]
alldata[[i]] <- result
}
alldata <- do.call("rbind", alldata)
Reorganizamos el dataframe del paso anterior:
fuel_fix_vel <- alldata %>%
select(speed,year,month,fuel) %>%
arrange(speed,year)
fuel_fix_vel
## # A tibble: 2,100 x 4
## speed year month fuel
## <dbl> <int> <fct> <dbl>
## 1 5.14 1993 V1 74419.
## 2 5.14 1993 V2 72284.
## 3 5.14 1993 V3 74083.
## 4 5.14 1993 V4 82392.
## 5 5.14 1993 V5 79016.
## 6 5.14 1993 V6 70112.
## 7 5.14 1993 V7 70435.
## 8 5.14 1993 V8 72632.
## 9 5.14 1993 V9 81863.
## 10 5.14 1993 V10 83129.
## # ... with 2,090 more rows
Comprobamos los valores de velocidad:
speed_values <- unique(fuel_fix_vel$speed)
speed_values
## [1] 5.144440 5.658884 6.173328 6.687772 7.202216 7.716660 8.231104
Podemos filtrar los datos para una velocidad en particular, p, ejplo 6.687772:
fuel_speed <- fuel_fix_vel %>%
filter(speed==6.687772) %>%
arrange(year)
head(fuel_speed,24)
## # A tibble: 24 x 4
## speed year month fuel
## <dbl> <int> <fct> <dbl>
## 1 6.69 1993 V1 118462.
## 2 6.69 1993 V2 113704.
## 3 6.69 1993 V3 116883.
## 4 6.69 1993 V4 123044.
## 5 6.69 1993 V5 121851.
## 6 6.69 1993 V6 111223.
## 7 6.69 1993 V7 111604.
## 8 6.69 1993 V8 113629.
## 9 6.69 1993 V9 122693.
## 10 6.69 1993 V10 124808.
## # ... with 14 more rows
Los datos de la columna fuel para cada velocidad a lo largo del tiempo forman nuestras series temporales. Las almacenamos en una lista, con las etiquetas de cada velocidad:
get_fuel_consump_speed_ts <- function(speed_vector) {
fuel_consump_speed_ts <- list()
for (v in 1:length(speed_vector)) {
fuel_speed <- fuel_fix_vel %>%
filter(speed==speed_vector[v]) %>%
arrange(year) %>%
select(fuel)
lbl <- as.character(round(speed_vector[v],2))
fuel_consump_speed_ts[[lbl]] <- ts(fuel_speed$fuel, start = 1993, frequency=12)
}
return(fuel_consump_speed_ts)
}
fuel_cons_speed_ts <- get_fuel_consump_speed_ts(speed_values)
str(fuel_cons_speed_ts)
## List of 7
## $ 5.14: Time-Series [1:300] from 1993 to 2018: 74419 72284 74083 82392 79016 ...
## $ 5.66: Time-Series [1:300] from 1993 to 2018: 87682 84794 87011 95237 91745 ...
## $ 6.17: Time-Series [1:300] from 1993 to 2018: 101287 98758 100594 107780 105096 ...
## $ 6.69: Time-Series [1:300] from 1993 to 2018: 118462 113704 116883 123044 121851 ...
## $ 7.2 : Time-Series [1:300] from 1993 to 2018: 138978 133587 137287 143542 140673 ...
## $ 7.72: Time-Series [1:300] from 1993 to 2018: 166930 160549 164553 172546 167334 ...
## $ 8.23: Time-Series [1:300] from 1993 to 2018: 209477 197287 203615 212766 206718 ...
Para verificar representamos para una de las velocidades:
plot(fuel_cons_speed_ts$`5.14`)
Finalmente y a partir de estas series temporales obtenemos las tendencias para cada velocidad:
get_fuel_consump_speed_trends <- function(l) {
get_trend_from_ts <- function(tseries) {
tseries.dc <- stl(tseries, s.window = "periodic", na.action = na.omit)
trend <- tseries.dc$time.series[,"trend"]
return(trend)
}
fuel_consump_speed_trends <- lapply(l,FUN = get_trend_from_ts)
return(fuel_consump_speed_trends)
}
fuel_cons_speed_trends <- get_fuel_consump_speed_trends(fuel_cons_speed_ts)
str(fuel_cons_speed_trends)
## List of 7
## $ 5.14: Time-Series [1:300] from 1993 to 2018: 76822 76815 76809 76784 76760 ...
## $ 5.66: Time-Series [1:300] from 1993 to 2018: 89414 89411 89407 89385 89363 ...
## $ 6.17: Time-Series [1:300] from 1993 to 2018: 102389 102500 102610 102703 102795 ...
## $ 6.69: Time-Series [1:300] from 1993 to 2018: 118224 118350 118477 118579 118681 ...
## $ 7.2 : Time-Series [1:300] from 1993 to 2018: 137940 138095 138251 138378 138505 ...
## $ 7.72: Time-Series [1:300] from 1993 to 2018: 164659 165002 165345 165654 165964 ...
## $ 8.23: Time-Series [1:300] from 1993 to 2018: 203656 204134 204611 205045 205478 ...
Representamos la tendencia correspondiente a la gráfica anterior:
plot(fuel_cons_speed_trends$`8.23`)
Observando el mapa con coordenadas se proponen los siguientes puntos intermedios (lat,lon) por orden desde Bimini hasta Bishop:
(30,-75) …… (35,-55) …… (40,-35) …… (45,-15)
Creamos la variable de clase SpatialPoints con dichos puntos:
latitude <- c(30,35,40,45)
longitude <- c(-75,-55,-35,-15)
mesoc_points <- data.frame(longitude,latitude)
mesoc_points
## longitude latitude
## 1 -75 30
## 2 -55 35
## 3 -35 40
## 4 -15 45
coordinates(mesoc_points) <- ~ longitude + latitude
proj4string(mesoc_points) <- CRS("+proj=longlat +datum=WGS84")
mesoc_points <- spTransform(mesoc_points, CRS(proj4string(mean_wave_direc)))
raster::plot(mean_wave_direc$X1993.01.01)
raster::plot(mesoc_points, add = TRUE)
raster::text(mesoc_points,labels=c("1","2","3","4"), add=TRUE,pos=1)
## Warning in text.default(xy[, 1], xy[, 2], labels, ...): "add" is not a
## graphical parameter
Probamos a realizar por un lado la extraccion con una de las variables, y las posteriores operaciones para obtener los datos de la serie temporal de dicha variable para los 4 puntos geográficos elegidos:
points_mean_wave_direc <- raster::extract(mean_wave_direc,mesoc_points,df=TRUE,along=TRUE,cellnumbers=TRUE)
points_mean_wave_direc
## ID cells X1993.01.01 X1993.02.01 X1993.03.01 X1993.04.01 X1993.05.01
## 1 1 28991 86.08797 353.339036 17.06821 27.64311 90.98813
## 2 2 26621 296.61912 274.480497 255.45666 10.22886 36.09753
## 3 3 24251 292.33422 322.114241 327.67362 312.02826 357.25587
## 4 4 21881 279.39714 5.021062 302.82672 304.77690 329.05797
## X1993.06.01 X1993.07.01 X1993.08.01 X1993.09.01 X1993.10.01 X1993.11.01
## 1 111.1161 138.4900 109.5890 96.21790 75.08458 70.56896
## 2 338.7100 253.2813 219.5185 26.20382 21.83103 324.89393
## 3 276.8647 236.3834 276.3812 307.67195 341.21498 304.43630
## 4 293.3505 317.7195 346.4557 301.18967 15.48060 299.03075
## X1993.12.01 X1994.01.01 X1994.02.01 X1994.03.01 X1994.04.01 X1994.05.01
## 1 353.8554 93.49864 86.37363 29.31861 95.32247 85.38481
## 2 292.0815 288.34598 289.91710 289.07111 353.05887 222.75962
## 3 270.8274 287.25278 288.35696 268.43770 336.61147 310.50108
## 4 299.9207 297.39919 270.11320 298.89341 319.21370 298.67916
## X1994.06.01 X1994.07.01 X1994.08.01 X1994.09.01 X1994.10.01 X1994.11.01
## 1 116.1976 127.7723 106.1336 96.17945 67.54207 80.74833
## 2 109.6879 167.3087 157.1898 322.01536 351.09771 324.64672
## 3 319.5598 298.3056 312.3853 243.51389 309.99568 296.19063
## 4 321.6253 288.0603 305.1999 320.52663 288.14272 273.34335
## X1994.12.01 X1995.01.01 X1995.02.01 X1995.03.01 X1995.04.01 X1995.05.01
## 1 61.72999 335.7270 341.3194 45.09032 76.36456 60.4610
## 2 313.08850 280.8419 271.2888 355.21230 321.13641 298.0254
## 3 301.73902 275.3210 276.7603 320.13660 247.35382 266.0535
## 4 281.22097 284.1490 280.2761 320.81779 329.05797 277.5458
## X1995.06.01 X1995.07.01 X1995.08.01 X1995.09.01 X1995.10.01 X1995.11.01
## 1 127.612992 129.5302 93.18002 78.82013 93.79529 36.19092
## 2 49.386201 167.3966 253.01208 97.22870 34.63628 342.85203
## 3 344.472601 301.6841 273.57957 240.56391 335.62814 342.93993
## 4 6.608671 298.7616 310.70434 324.93238 278.61707 289.29085
## X1995.12.01 X1996.01.01 X1996.02.01 X1996.03.01 X1996.04.01 X1996.05.01
## 1 16.92538 67.66292 329.0360 11.7066 95.19612 98.66798
## 2 297.99798 321.60884 260.2525 287.0385 317.78540 349.93309
## 3 277.80953 323.66889 296.9652 305.8976 300.47003 326.41012
## 4 286.63751 293.98226 329.9040 295.3666 298.81650 319.86192
## X1996.06.01 X1996.07.01 X1996.08.01 X1996.09.01 X1996.10.01 X1996.11.01
## 1 117.2633 160.4364 115.0495 96.77274 63.2352 39.02555
## 2 266.5644 198.6214 132.5406 303.35959 358.2447 325.90472
## 3 281.4517 286.3738 292.6748 293.05387 306.4469 301.17319
## 4 288.9503 317.0438 311.5723 326.17390 293.2626 307.11711
## X1996.12.01 X1997.01.01 X1997.02.01 X1997.03.01 X1997.04.01 X1997.05.01
## 1 51.58908 24.2097 84.46191 101.5081 15.68935 103.1891
## 2 16.59577 302.6949 280.19918 287.0935 294.18003 279.3532
## 3 340.91284 302.1895 283.82486 285.1103 269.11889 322.3450
## 4 331.12351 299.0637 282.03400 289.9061 23.44062 318.8346
## X1997.06.01 X1997.07.01 X1997.08.01 X1997.09.01 X1997.10.01 X1997.11.01
## 1 109.1879 136.9738 129.6895 99.00308 49.89709 4.054214
## 2 330.3984 245.6948 206.6528 334.39761 330.25005 314.181693
## 3 332.0354 252.8692 301.4698 340.87439 296.25655 303.227745
## 4 318.8786 303.9309 291.6970 302.61248 280.42991 289.274370
## X1997.12.01 X1998.01.01 X1998.02.01 X1998.03.01 X1998.04.01 X1998.05.01
## 1 321.0870 70.90956 2.230388 88.38973 85.24198 142.5222
## 2 281.6604 304.14515 284.121510 316.59332 244.98065 232.6973
## 3 270.5582 305.50753 302.826722 297.91557 310.31430 249.0019
## 4 259.8075 292.03208 290.636747 324.72913 306.42494 351.3504
## X1998.06.01 X1998.07.01 X1998.08.01 X1998.09.01 X1998.10.01 X1998.11.01
## 1 159.7058 146.9994 106.0951 99.86006 69.98665 77.7544
## 2 244.8049 212.8494 173.4998 329.64027 342.78062 309.9792
## 3 297.5530 290.0654 296.6411 302.95857 263.04862 299.4043
## 4 284.0171 312.1162 327.1133 301.68958 304.63407 308.0125
## X1998.12.01 X1999.01.01 X1999.02.01 X1999.03.01 X1999.04.01 X1999.05.01
## 1 87.36245 82.24805 10.04208 2.812694 52.79214 77.34239
## 2 293.14176 332.91437 304.89226 273.936645 285.35204 28.79124
## 3 289.64243 300.32720 219.60089 322.295525 269.82205 344.47809
## 4 285.64869 290.29615 317.30747 322.015359 296.40487 316.07144
## X1999.06.01 X1999.07.01 X1999.08.01 X1999.09.01 X1999.10.01 X1999.11.01
## 1 99.68976 139.7535 154.3002 94.48746 74.39241 61.82888
## 2 192.57858 255.8247 205.2630 63.44944 30.15362 31.14793
## 3 322.89980 288.8733 296.5532 321.97690 321.58687 7.64144
## 4 333.10115 308.1224 287.0495 297.72330 310.57249 325.57512
## X1999.12.01 X2000.01.01 X2000.02.01 X2000.03.01 X2000.04.01 X2000.05.01
## 1 39.16838 42.61826 35.00434 13.37661 77.22703 111.8907
## 2 331.72229 280.36399 292.64186 319.14778 301.85987 264.6582
## 3 251.20473 300.90951 289.93908 202.74148 355.47599 310.5176
## 4 295.86102 336.38074 288.88433 343.07726 334.82060 314.2971
## X2000.06.01 X2000.07.01 X2000.08.01 X2000.09.01 X2000.10.01 X2000.11.01
## 1 105.1173 147.0159 112.90701 85.06069 41.70086 21.18281
## 2 317.7579 209.6962 66.78946 78.16092 31.88405 284.84665
## 3 299.5087 350.2572 275.27155 333.86474 312.89073 319.28511
## 4 300.4700 335.0623 313.28077 304.24403 301.28856 311.01197
## X2000.12.01 X2001.01.01 X2001.02.01 X2001.03.01 X2001.04.01 X2001.05.01
## 1 48.77643 342.0445 87.68107 23.92953 43.07971 84.41796
## 2 313.91801 283.7095 304.44180 298.40998 305.17792 326.65733
## 3 299.66799 280.2047 279.31474 285.05540 266.81713 280.42991
## 4 272.42595 294.2405 298.95384 268.55855 298.58028 300.25029
## X2001.06.01 X2001.07.01 X2001.08.01 X2001.09.01 X2001.10.01 X2001.11.01
## 1 136.4025 129.4698 113.4454 86.71422 61.65308 61.83437
## 2 127.2339 187.5905 174.6974 334.54043 344.82968 24.13279
## 3 279.4576 281.3034 303.6398 349.06513 330.54120 131.99128
## 4 304.1506 297.1630 298.9044 344.40119 289.16999 345.31310
## X2001.12.01 X2002.01.01 X2002.02.01 X2002.03.01 X2002.04.01 X2002.05.01
## 1 47.99636 358.2172 40.64062 85.76935 85.2255 92.61969
## 2 316.84602 287.6483 299.13512 349.66941 339.3637 311.44595
## 3 333.59007 279.7487 277.83150 319.43893 315.5221 298.08587
## 4 24.16026 263.9770 299.41529 305.72177 307.3588 296.04780
## X2002.06.01 X2002.07.01 X2002.08.01 X2002.09.01 X2002.10.01 X2002.11.01
## 1 122.9985 133.1614 99.81062 86.5549136 56.37387 32.82344
## 2 255.3358 221.1171 149.35059 72.1730498 23.74276 299.79434
## 3 282.6932 279.5400 291.05425 338.4407877 332.46940 291.11468
## 4 297.6684 311.5338 313.86307 0.6592595 294.80628 286.98909
## X2002.12.01 X2003.01.01 X2003.02.01 X2003.03.01 X2003.04.01 X2003.05.01
## 1 338.7484 321.3067 63.33408 77.3314 76.71614 107.8475
## 2 298.0254 278.6885 280.45188 337.5234 322.91629 334.2713
## 3 290.9499 274.7991 296.24007 312.0228 329.10741 286.4782
## 4 267.0973 290.4829 287.04403 303.6782 282.86900 295.7237
## X2003.06.01 X2003.07.01 X2003.08.01 X2003.09.01 X2003.10.01 X2003.11.01
## 1 139.2262 130.2883 124.3773 85.91767 63.10885 61.42236
## 2 269.6572 248.5294 326.9650 73.27723 78.65533 16.01896
## 3 289.5435 299.0967 307.2929 319.13679 20.49063 312.84678
## 4 283.1656 287.3297 310.0726 309.63311 349.62546 293.68012
## X2003.12.01 X2004.01.01 X2004.02.01 X2004.03.01 X2004.04.01 X2004.05.01
## 1 23.29779 335.1832 41.78875 53.46234 75.49659 111.4787
## 2 298.08038 287.0330 296.39389 314.25311 348.52677 333.7659
## 3 304.22206 262.0049 294.71838 299.11864 336.19396 333.1396
## 4 303.31564 282.2977 311.66569 294.88319 319.52133 331.8431
## X2004.06.01 X2004.07.01 X2004.08.01 X2004.09.01 X2004.10.01 X2004.11.01
## 1 144.9833 146.2358 125.0915 111.72592 44.74423 43.46975
## 2 298.7176 182.8167 133.3646 52.73721 17.65601 347.47752
## 3 274.9749 296.0148 322.9163 317.57115 12.09663 307.66646
## 4 290.2083 299.9317 289.3293 316.82954 317.79089 341.88518
## X2004.12.01 X2005.01.01 X2005.02.01 X2005.03.01 X2005.04.01 X2005.05.01
## 1 41.85467 55.80805 19.111773 316.5549 53.10527 74.51326
## 2 317.70300 331.22788 329.442509 298.7780 338.00131 28.76927
## 3 283.40736 292.25732 159.486015 285.4509 328.43171 355.55839
## 4 306.01842 313.90153 2.625916 238.2182 301.40941 315.48913
## X2005.06.01 X2005.07.01 X2005.08.01 X2005.09.01 X2005.10.01 X2005.11.01
## 1 114.51110 119.7079 105.3425 74.51875 84.549803 69.426323
## 2 49.02363 175.8675 115.0604 72.87072 8.333615 26.286225
## 3 294.97658 304.5517 295.3666 300.70076 321.284729 5.235307
## 4 295.85553 349.1091 339.5230 305.40315 278.342396 334.293230
## X2005.12.01 X2006.01.01 X2006.02.01 X2006.03.01 X2006.04.01 X2006.05.01
## 1 35.02631 62.31779 350.1748 359.9312 65.27876 109.9625
## 2 294.61950 273.97510 284.5335 286.8573 317.28549 103.3924
## 3 312.45675 282.42403 283.8963 273.4203 339.18790 301.6401
## 4 322.92727 302.53008 312.2260 275.0463 343.67056 273.4258
## X2006.06.01 X2006.07.01 X2006.08.01 X2006.09.01 X2006.10.01 X2006.11.01
## 1 123.2347 128.6073 124.8608 76.88643 61.3015 61.07078
## 2 138.2813 195.6549 310.4516 102.00800 336.5346 63.83399
## 3 349.8067 301.6072 165.4409 309.66607 330.6291 355.39908
## 4 336.1006 295.9544 321.5100 278.34789 289.7193 315.92861
## X2006.12.01 X2007.01.01 X2007.02.01 X2007.03.01 X2007.04.01 X2007.05.01
## 1 91.23534 46.15605 312.9731 71.60722 45.76601 91.26280
## 2 316.61529 288.75799 274.9419 330.40936 303.96936 54.31383
## 3 283.22608 274.19484 273.4972 269.23974 258.30777 296.00935
## 4 276.10107 296.11921 279.2928 302.81024 320.93315 302.37077
## X2007.06.01 X2007.07.01 X2007.08.01 X2007.09.01 X2007.10.01 X2007.11.01
## 1 113.3630 125.9045 107.2872 86.79113 91.669320 51.87474
## 2 222.8860 127.8767 193.6992 25.81379 1.626108 349.03217
## 3 299.1846 305.2878 251.9409 239.80581 308.364124 22.65505
## 4 290.0160 288.9997 348.2411 28.64841 290.559839 24.77552
## X2007.12.01 X2008.01.01 X2008.02.01 X2008.03.01 X2008.04.01 X2008.05.01
## 1 87.6536 55.58831 149.5429 62.94954 70.7008 84.12681
## 2 304.4308 270.17363 276.6394 300.15141 330.0578 260.94463
## 3 295.8061 298.98680 307.3698 279.71576 327.5692 296.49826
## 4 281.8692 281.63298 285.2092 312.79185 309.4628 286.73639
## X2008.06.01 X2008.07.01 X2008.08.01 X2008.09.01 X2008.10.01 X2008.11.01
## 1 119.3618 124.9926 147.7630 86.868040 58.9558 55.50042
## 2 287.6977 140.7259 205.0762 50.885914 351.9712 26.03902
## 3 249.5567 307.1226 290.3511 3.823489 302.5081 70.54149
## 4 296.6741 309.1662 299.5361 351.322938 309.2266 338.60010
## X2008.12.01 X2009.01.01 X2009.02.01 X2009.03.01 X2009.04.01 X2009.05.01
## 1 77.01278 358.0030 21.42452 56.42881 150.1416 113.44537
## 2 355.02553 269.7781 279.13345 356.99218 294.1471 45.99674
## 3 309.23758 283.5447 282.75364 327.38246 304.1012 340.17672
## 4 316.01101 276.0461 312.27546 324.10836 292.1420 311.42947
## X2009.06.01 X2009.07.01 X2009.08.01 X2009.09.01 X2009.10.01 X2009.11.01
## 1 140.9896 150.0647 117.2523 73.3871 75.43616 52.33619
## 2 242.2779 227.6982 335.1337 66.7620 336.44666 86.17037
## 3 310.4407 304.8318 299.3109 313.9125 318.51053 310.72082
## 4 267.8005 281.2979 289.9171 356.5967 277.55683 285.46191
## X2009.12.01 X2010.01.01 X2010.02.01 X2010.03.01 X2010.04.01 X2010.05.01
## 1 57.41763 331.0466 322.5153 346.6260 62.32329 108.6331
## 2 301.67859 291.6750 286.7639 314.3190 317.86231 299.9591
## 3 310.52855 273.9147 286.2969 313.0940 344.26934 272.6347
## 4 295.66875 294.9876 270.9372 274.3432 305.92503 329.3986
## X2010.06.01 X2010.07.01 X2010.08.01 X2010.09.01 X2010.10.01 X2010.11.01
## 1 117.7193 125.7947 98.97561 86.59337 86.3956 48.023824
## 2 247.1725 208.6195 182.85517 168.47880 260.8622 353.470879
## 3 267.2456 299.1571 239.03124 308.28172 308.9300 6.487815
## 4 301.5303 302.1895 306.35352 304.13416 318.0546 320.389296
## X2010.12.01 X2011.01.01 X2011.02.01 X2011.03.01 X2011.04.01 X2011.05.01
## 1 343.978190 329.0525 70.25034 45.167228 108.1222 70.62939
## 2 296.207110 284.8357 289.36776 3.191742 20.1775 102.36508
## 3 9.047765 289.9501 278.93020 305.776707 319.7081 21.01251
## 4 50.171765 312.3908 275.32100 334.040531 302.9531 322.10875
## X2011.06.01 X2011.07.01 X2011.08.01 X2011.09.01 X2011.10.01 X2011.11.01
## 1 107.4136 130.6783 153.6080 91.30126 87.42288 57.08802
## 2 302.7169 198.9345 237.7787 11.08584 343.66506 23.06157
## 3 222.6223 278.3644 286.7034 310.63841 328.16803 312.66550
## 4 307.1226 312.5831 301.5248 286.48919 302.12905 280.23214
## X2011.12.01 X2012.01.01 X2012.02.01 X2012.03.01 X2012.04.01 X2012.05.01
## 1 57.29678 35.30648 45.99674 100.0029 27.15419 111.05572
## 2 337.71016 291.95518 282.82505 317.0877 251.49039 35.69651
## 3 270.69551 268.08612 250.84766 286.9397 301.85438 351.51521
## 4 300.39312 298.61874 304.28249 290.7741 332.04091 316.86250
## X2012.06.01 X2012.07.01 X2012.08.01 X2012.09.01 X2012.10.01 X2012.11.01
## 1 97.71761 144.7745 140.3523 82.00633 84.87392 26.879518
## 2 291.43330 217.2387 142.6870 89.32361 12.56907 8.459965
## 3 288.02736 296.0039 337.1553 16.06291 330.68403 331.568476
## 4 274.49148 306.4030 272.6622 349.14753 300.26128 322.328486
## X2012.12.01 X2013.01.01 X2013.02.01 X2013.03.01 X2013.04.01 X2013.05.01
## 1 88.35677 45.87588 353.4379 333.4417 94.047985 92.03738
## 2 322.80092 308.23778 285.8465 306.0788 6.740514 39.65729
## 3 294.44920 255.87417 271.2998 326.8276 329.975374 37.35004
## 4 274.90349 277.46344 288.4778 293.0264 302.249910 333.13411
## X2013.06.01 X2013.07.01 X2013.08.01 X2013.09.01 X2013.10.01 X2013.11.01
## 1 133.1888 137.5507 107.8091 77.97963 62.10904 62.098054
## 2 260.7853 321.8560 204.6752 174.10959 346.42827 8.811546
## 3 287.2363 272.8544 273.4697 321.60884 295.33914 289.961052
## 4 322.3889 335.0568 306.7600 315.98904 270.88228 335.677580
## X2013.12.01 X2014.01.01 X2014.02.01 X2014.03.01 X2014.04.01 X2014.05.01
## 1 87.25258 326.5420 88.97203 17.23851 60.67525 83.64888
## 2 307.51813 262.5377 272.19522 290.01599 330.63459 301.18967
## 3 311.68217 292.3452 290.76859 283.97868 312.95665 281.48465
## 4 287.91749 290.8730 283.97868 295.65776 301.39842 308.90798
## X2014.06.01 X2014.07.01 X2014.08.01 X2014.09.01 X2014.10.01 X2014.11.01
## 1 120.4001 145.7414 143.9395 94.86102 57.65934 54.86317
## 2 230.7251 192.1776 225.8030 50.50687 30.93918 285.59376
## 3 283.7095 335.4853 256.9564 346.37334 332.49138 314.73104
## 4 299.7943 308.9684 304.8923 315.41772 295.06447 294.89417
## X2014.12.01 X2015.01.01 X2015.02.01 X2015.03.01 X2015.04.01 X2015.05.01
## 1 52.71523 46.8702 1.845846 71.60173 74.25507 88.93358
## 2 20.35329 257.0003 266.130447 290.07641 322.19664 351.99314
## 3 75.85366 279.5564 267.306046 269.75613 282.01202 337.44098
## 4 314.80795 296.8224 323.800732 301.33800 313.45656 301.62915
## X2015.06.01 X2015.07.01 X2015.08.01 X2015.09.01 X2015.10.01 X2015.11.01
## 1 112.0610 171.4398 119.8727 83.84664 71.7116 78.69378
## 2 290.8840 274.0575 120.8396 67.56953 325.8608 345.03293
## 3 299.1955 274.0190 336.4027 333.71092 340.9238 267.65213
## 4 318.6424 280.7540 299.4867 332.52983 342.7037 285.88491
## X2015.12.01 X2016.01.01 X2016.02.01 X2016.03.01 X2016.04.01 X2016.05.01
## 1 97.86594 21.29267 42.43698 71.09084 54.011689 109.7867
## 2 330.95321 272.60723 286.71991 301.10727 8.591808 298.3386
## 3 295.38309 280.66613 289.97204 264.36705 328.107600 309.2925
## 4 251.90789 278.03476 300.92049 306.62270 313.731229 302.1510
## X2016.06.01 X2016.07.01 X2016.08.01 X2016.09.01 X2016.10.01 X2016.11.01
## 1 119.1311 137.2540 107.12790 105.4304 63.68566 42.8380
## 2 249.7490 215.8544 10.93202 80.0177 3.99928 302.6839
## 3 291.3674 269.4100 285.23668 293.6087 332.46940 313.9620
## 4 273.6070 301.9093 298.05291 287.4560 308.96291 338.5836
## X2016.12.01 X2017.01.01 X2017.02.01 X2017.03.01 X2017.04.01 X2017.05.01
## 1 79.33651 346.4228 49.6389 58.31306 86.09896 136.0290
## 2 288.51627 301.7994 281.2649 291.76291 357.88762 318.6479
## 3 285.67616 305.3372 276.0297 280.95728 27.11574 293.2461
## 4 272.58526 296.1961 286.4178 293.11979 18.66131 256.1269
## X2017.06.01 X2017.07.01 X2017.08.01 X2017.09.01 X2017.10.01 X2017.11.01
## 1 124.5476 127.7888 124.6575 69.87129 80.30886 61.29601
## 2 290.1368 195.6659 219.0296 222.84752 22.34742 13.17884
## 3 308.3147 269.8440 288.3899 280.95179 298.55831 11.61321
## 4 294.7074 301.2116 298.7561 296.60813 288.14821 330.98068
## X2017.12.01
## 1 29.63174
## 2 302.39274
## 3 266.27328
## 4 297.33876
Comprobamos que los puntos siguen nuestro orden requerido:
# lonlat from cells
cells_lonlat <- xyFromCell(mean_wave_direc,points_mean_wave_direc$cells)
cells_lonlat
## x y
## [1,] -75 30
## [2,] -55 35
## [3,] -35 40
## [4,] -15 45
Damos formato para tener las series temporales en cada punto como columnas:
index <- points_mean_wave_direc$ID
df_aux <- points_mean_wave_direc %>%
dplyr::select(-ID,-cells)
row.names(df_aux) <- index
df_aux <- as.data.frame(t(df_aux))
head(df_aux)
## 1 2 3 4
## X1993.01.01 86.08797 296.61912 292.3342 279.397139
## X1993.02.01 353.33904 274.48050 322.1142 5.021062
## X1993.03.01 17.06821 255.45666 327.6736 302.826722
## X1993.04.01 27.64311 10.22886 312.0283 304.776899
## X1993.05.01 90.98813 36.09753 357.2559 329.057967
## X1993.06.01 111.11615 338.70997 276.8647 293.350513
Finalmente se obtiene con éxito como resultado las series temporales de la variable de prueba en forma de lista. Es decir de cada variable mesoceánica tendremos una lista con las series temporales (1993 - 2017) en cada uno de los puntos elegidos:
col_to_ts <- function(c) {
c_ts <- ts(c, start = 1993, frequency=12)
return(c_ts)
}
final_test <- lapply(df_aux,FUN = col_to_ts)
str(final_test)
## List of 4
## $ 1: Time-Series [1:300] from 1993 to 2018: 86.1 353.3 17.1 27.6 91 ...
## $ 2: Time-Series [1:300] from 1993 to 2018: 296.6 274.5 255.5 10.2 36.1 ...
## $ 3: Time-Series [1:300] from 1993 to 2018: 292 322 328 312 357 ...
## $ 4: Time-Series [1:300] from 1993 to 2018: 279.4 5.02 302.83 304.78 329.06 ...
mesocvar_ts_from_points <- function(mesoc_rbrick,sp_points) {
col_to_ts <- function(c) {
c_ts <- ts(c, start = 1993, frequency=12)
return(c_ts)
}
df_extract_points <- raster::extract(mesoc_rbrick,sp_points,df=TRUE,along=TRUE)
index <- df_extract_points$ID
df_aux <- df_extract_points %>%
dplyr::select(-ID)
row.names(df_aux) <- index
df_aux <- as.data.frame(t(df_aux))
l_mesoc_ts_points <- lapply(df_aux,FUN = col_to_ts)
return(l_mesoc_ts_points)
}
Obtenemos la primera de las variables (u-component wind 10 meters) y observamos su estructura, con las series temporales correspondientes a cada punto:
u10.wind_pts_ts <- mesocvar_ts_from_points(u_wind_comp_10,mesoc_points)
str(u10.wind_pts_ts)
## List of 4
## $ 1: Time-Series [1:300] from 1993 to 2018: -1.68 2.25 1.13 1.01 -1.54 ...
## $ 2: Time-Series [1:300] from 1993 to 2018: 4.7748 1.7727 2.4426 0.0225 0.4191 ...
## $ 3: Time-Series [1:300] from 1993 to 2018: 4.74 -2.03 2.95 1.17 1.53 ...
## $ 4: Time-Series [1:300] from 1993 to 2018: 4.3882 -5.2959 -0.9756 4.8805 -0.0784 ...
Obtenemos el resto de variables:
v10.wind_pts_ts <- mesocvar_ts_from_points(v_wind_comp_10,mesoc_points)
m.wave.dir_pts_ts <- mesocvar_ts_from_points(mean_wave_direc,mesoc_points)
m.wave.period_pts_ts <- mesocvar_ts_from_points(mean_wave_period,mesoc_points)
sig.wave.height_pts_ts <- mesocvar_ts_from_points(signif_wave_height,mesoc_points)
bfi_pts_ts <- mesocvar_ts_from_points(bfi_index,mesoc_points)
peak.wave.period_pts_ts <- mesocvar_ts_from_points(peak_wave_period,mesoc_points)
wave.spectr.kurt_pts_ts <- mesocvar_ts_from_points(wave_spectral_kurtosis,mesoc_points)
max.indiv.wave.height_pts_ts <- mesocvar_ts_from_points(max_individual_wave_height,mesoc_points)
Procedemos a la descomposición en componentes (tendencia,estacional,residuos) de las series temporales en los puntos etiquetados como “1” y “3”, para la variable “mean wave direction”:
m.wave.dir.pt1.dc <- stl(m.wave.dir_pts_ts$`1`, s.window = "periodic", na.action = na.omit)
m.wave.dir.pt3.dc <- stl(m.wave.dir_pts_ts$`3`, s.window = "periodic", na.action = na.omit)
plot(m.wave.dir.pt1.dc)
plot(m.wave.dir.pt3.dc)
Mostramos los valores para cada punto:
boxplot(m.wave.dir_pts_ts$`1`)
boxplot(m.wave.dir_pts_ts$`3`)
Es de interés para el posterior análisis obtener la tendencia de las series temporales (trend component) para las variables en cada punto:
plot(m.wave.dir.pt3.dc$time.series[,"trend"])
get_mesoc_pts_trend_from_ts <- function(l) {
get_trend_from_ts <- function(tseries) {
tseries.dc <- stl(tseries, s.window = "periodic", na.action = na.omit)
trend <- tseries.dc$time.series[,"trend"]
return(trend)
}
mesoc_pts_trend <- lapply(l,FUN = get_trend_from_ts)
return(mesoc_pts_trend)
}
Guardamos los datos de tendencia de todas las variables, para los puntos geográficos elegidos. De nuevo para cada variable se almacena en forma de lista las distintas tendencia de cada uno de los puntos:
u10.wind_pts_trend <- get_mesoc_pts_trend_from_ts(u10.wind_pts_ts)
v10.wind_pts_trend <- get_mesoc_pts_trend_from_ts(v10.wind_pts_ts)
m.wave.dir_pts_trend <- get_mesoc_pts_trend_from_ts(m.wave.dir_pts_ts)
m.wave.period_pts_trend <- get_mesoc_pts_trend_from_ts(m.wave.period_pts_ts)
sig.wave.height_pts_trend <- get_mesoc_pts_trend_from_ts(sig.wave.height_pts_ts)
bfi_pts_trend <- get_mesoc_pts_trend_from_ts(bfi_pts_ts)
peak.wave.period_pts_trend <- get_mesoc_pts_trend_from_ts(peak.wave.period_pts_ts)
wave.spectr.kurt_pts_trend <- get_mesoc_pts_trend_from_ts(wave.spectr.kurt_pts_ts)
max.indiv.wave.height_pts_trend <- get_mesoc_pts_trend_from_ts(max.indiv.wave.height_pts_ts)
Como muestra observamos la tendencia de la variable “mean wave direction” en los puntos etiquetados “1” y “3”:
str(m.wave.dir_pts_trend)
## List of 4
## $ 1: Time-Series [1:300] from 1993 to 2018: 105 106 108 109 111 ...
## $ 2: Time-Series [1:300] from 1993 to 2018: 164 169 174 180 185 ...
## $ 3: Time-Series [1:300] from 1993 to 2018: 306 305 304 303 302 ...
## $ 4: Time-Series [1:300] from 1993 to 2018: 235 240 245 249 254 ...
class(m.wave.dir_pts_trend$`1`)
## [1] "ts"
plot(m.wave.dir_pts_trend$`1`)
plot(m.wave.dir_pts_trend$`3`)
Pasamos a analizar las tendencias del consumo de fuel para las distintas velocidades.
fuel_speed_trends_df <- as.data.frame(fuel_cons_speed_trends) %>%
mutate(year=as.character(floor(time(fuel_cons_speed_trends$`5.14`)))) %>%
mutate(month=as.character(cycle(fuel_cons_speed_trends$`5.14`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything())
head(fuel_speed_trends_df,25)
## date X5.14 X5.66 X6.17 X6.69 X7.2 X7.72
## 1 1993-01-01 76821.84 89414.25 102388.6 118223.6 137939.6 164659.2
## 2 1993-02-01 76815.23 89410.58 102499.5 118350.0 138095.5 165001.9
## 3 1993-03-01 76808.62 89406.92 102610.4 118476.5 138251.3 165344.6
## 4 1993-04-01 76784.33 89384.82 102702.9 118579.0 138378.2 165654.3
## 5 1993-05-01 76760.03 89362.72 102795.4 118681.5 138505.1 165964.1
## 6 1993-06-01 76729.49 89334.77 102882.0 118776.4 138622.5 166263.9
## 7 1993-07-01 76698.95 89306.82 102968.7 118871.3 138739.8 166563.7
## 8 1993-08-01 76662.94 89273.57 103045.1 118963.2 138852.6 166847.9
## 9 1993-09-01 76626.93 89240.33 103121.6 119055.0 138965.3 167132.1
## 10 1993-10-01 76464.98 89073.17 103021.8 118971.9 138872.6 167108.4
## 11 1993-11-01 76303.04 88906.01 102922.1 118888.8 138779.9 167084.8
## 12 1993-12-01 76063.86 88652.77 102691.7 118650.7 138519.6 166813.3
## 13 1994-01-01 75824.69 88399.54 102461.3 118412.7 138259.3 166541.8
## 14 1994-02-01 75624.66 88184.36 102264.6 118201.2 138027.7 166275.2
## 15 1994-03-01 75424.63 87969.18 102068.0 117989.7 137796.1 166008.6
## 16 1994-04-01 75164.47 87715.14 101786.1 117718.6 137508.2 165607.6
## 17 1994-05-01 74904.31 87461.10 101504.2 117447.6 137220.2 165206.7
## 18 1994-06-01 74712.21 87284.28 101269.0 117278.2 137003.9 164853.4
## 19 1994-07-01 74520.10 87107.46 101033.8 117108.8 136787.5 164500.2
## 20 1994-08-01 74548.41 87144.84 101015.6 117189.6 136799.9 164385.4
## 21 1994-09-01 74576.71 87182.23 100997.5 117270.4 136812.2 164270.7
## 22 1994-10-01 74764.92 87385.17 101169.2 117510.5 137028.0 164391.3
## 23 1994-11-01 74953.12 87588.12 101340.9 117750.6 137243.8 164511.9
## 24 1994-12-01 75076.75 87729.44 101465.7 117910.2 137396.4 164621.8
## 25 1995-01-01 75200.38 87870.76 101590.6 118069.8 137549.1 164731.8
## X8.23
## 1 203655.9
## 2 204133.6
## 3 204611.4
## 4 205044.5
## 5 205477.7
## 6 205896.3
## 7 206314.9
## 8 206731.5
## 9 207148.1
## 10 207232.5
## 11 207317.0
## 12 207040.1
## 13 206763.1
## 14 206421.4
## 15 206079.6
## 16 205543.9
## 17 205008.2
## 18 204646.3
## 19 204284.4
## 20 204284.3
## 21 204284.2
## 22 204472.2
## 23 204660.2
## 24 204810.9
## 25 204961.7
fuel_speed_df_long <- fuel_speed_trends_df %>%
gather(key = "variable", value = "value", -date)
fuel_speed_df_long
## date variable value
## 1 1993-01-01 X5.14 76821.84
## 2 1993-02-01 X5.14 76815.23
## 3 1993-03-01 X5.14 76808.62
## 4 1993-04-01 X5.14 76784.33
## 5 1993-05-01 X5.14 76760.03
## 6 1993-06-01 X5.14 76729.49
## 7 1993-07-01 X5.14 76698.95
## 8 1993-08-01 X5.14 76662.94
## 9 1993-09-01 X5.14 76626.93
## 10 1993-10-01 X5.14 76464.98
## 11 1993-11-01 X5.14 76303.04
## 12 1993-12-01 X5.14 76063.86
## 13 1994-01-01 X5.14 75824.69
## 14 1994-02-01 X5.14 75624.66
## 15 1994-03-01 X5.14 75424.63
## 16 1994-04-01 X5.14 75164.47
## 17 1994-05-01 X5.14 74904.31
## 18 1994-06-01 X5.14 74712.21
## 19 1994-07-01 X5.14 74520.10
## 20 1994-08-01 X5.14 74548.41
## 21 1994-09-01 X5.14 74576.71
## 22 1994-10-01 X5.14 74764.92
## 23 1994-11-01 X5.14 74953.12
## 24 1994-12-01 X5.14 75076.75
## 25 1995-01-01 X5.14 75200.38
## 26 1995-02-01 X5.14 75321.77
## 27 1995-03-01 X5.14 75443.15
## 28 1995-04-01 X5.14 75654.41
## 29 1995-05-01 X5.14 75865.67
## 30 1995-06-01 X5.14 76158.07
## 31 1995-07-01 X5.14 76450.47
## 32 1995-08-01 X5.14 76723.02
## 33 1995-09-01 X5.14 76995.58
## 34 1995-10-01 X5.14 77150.15
## 35 1995-11-01 X5.14 77304.71
## 36 1995-12-01 X5.14 77351.91
## 37 1996-01-01 X5.14 77399.11
## 38 1996-02-01 X5.14 77336.40
## 39 1996-03-01 X5.14 77273.70
## 40 1996-04-01 X5.14 77220.57
## 41 1996-05-01 X5.14 77167.45
## 42 1996-06-01 X5.14 77124.96
## 43 1996-07-01 X5.14 77082.46
## 44 1996-08-01 X5.14 76761.23
## 45 1996-09-01 X5.14 76440.01
## 46 1996-10-01 X5.14 76080.01
## 47 1996-11-01 X5.14 75720.02
## 48 1996-12-01 X5.14 75572.99
## 49 1997-01-01 X5.14 75425.95
## 50 1997-02-01 X5.14 75301.65
## 51 1997-03-01 X5.14 75177.34
## 52 1997-04-01 X5.14 74796.93
## 53 1997-05-01 X5.14 74416.52
## 54 1997-06-01 X5.14 74186.98
## 55 1997-07-01 X5.14 73957.44
## 56 1997-08-01 X5.14 74301.95
## 57 1997-09-01 X5.14 74646.46
## 58 1997-10-01 X5.14 74933.39
## 59 1997-11-01 X5.14 75220.32
## 60 1997-12-01 X5.14 75174.80
## 61 1998-01-01 X5.14 75129.29
## 62 1998-02-01 X5.14 75058.45
## 63 1998-03-01 X5.14 74987.61
## 64 1998-04-01 X5.14 74787.85
## 65 1998-05-01 X5.14 74588.10
## 66 1998-06-01 X5.14 74438.36
## 67 1998-07-01 X5.14 74288.62
## 68 1998-08-01 X5.14 74336.73
## 69 1998-09-01 X5.14 74384.83
## 70 1998-10-01 X5.14 74661.07
## 71 1998-11-01 X5.14 74937.30
## 72 1998-12-01 X5.14 75225.60
## 73 1999-01-01 X5.14 75513.91
## 74 1999-02-01 X5.14 75921.21
## 75 1999-03-01 X5.14 76328.52
## 76 1999-04-01 X5.14 76686.94
## 77 1999-05-01 X5.14 77045.35
## 78 1999-06-01 X5.14 77041.55
## 79 1999-07-01 X5.14 77037.75
## 80 1999-08-01 X5.14 76902.04
## 81 1999-09-01 X5.14 76766.33
## 82 1999-10-01 X5.14 76857.31
## 83 1999-11-01 X5.14 76948.28
## 84 1999-12-01 X5.14 76976.67
## 85 2000-01-01 X5.14 77005.07
## 86 2000-02-01 X5.14 77010.10
## 87 2000-03-01 X5.14 77015.12
## 88 2000-04-01 X5.14 77166.79
## 89 2000-05-01 X5.14 77318.46
## 90 2000-06-01 X5.14 77528.11
## 91 2000-07-01 X5.14 77737.75
## 92 2000-08-01 X5.14 77798.11
## 93 2000-09-01 X5.14 77858.47
## 94 2000-10-01 X5.14 77794.69
## 95 2000-11-01 X5.14 77730.92
## 96 2000-12-01 X5.14 77641.65
## 97 2001-01-01 X5.14 77552.38
## 98 2001-02-01 X5.14 77339.67
## 99 2001-03-01 X5.14 77126.96
## 100 2001-04-01 X5.14 76888.87
## 101 2001-05-01 X5.14 76650.78
## 102 2001-06-01 X5.14 76477.74
## 103 2001-07-01 X5.14 76304.70
## 104 2001-08-01 X5.14 76302.03
## 105 2001-09-01 X5.14 76299.37
## 106 2001-10-01 X5.14 76355.50
## 107 2001-11-01 X5.14 76411.63
## 108 2001-12-01 X5.14 76444.16
## 109 2002-01-01 X5.14 76476.69
## 110 2002-02-01 X5.14 76471.87
## 111 2002-03-01 X5.14 76467.05
## 112 2002-04-01 X5.14 76326.24
## 113 2002-05-01 X5.14 76185.43
## 114 2002-06-01 X5.14 75795.19
## 115 2002-07-01 X5.14 75404.95
## 116 2002-08-01 X5.14 74942.50
## 117 2002-09-01 X5.14 74480.05
## 118 2002-10-01 X5.14 74246.90
## 119 2002-11-01 X5.14 74013.75
## 120 2002-12-01 X5.14 73989.09
## 121 2003-01-01 X5.14 73964.43
## 122 2003-02-01 X5.14 73985.91
## 123 2003-03-01 X5.14 74007.40
## 124 2003-04-01 X5.14 74158.09
## 125 2003-05-01 X5.14 74308.78
## 126 2003-06-01 X5.14 74483.10
## 127 2003-07-01 X5.14 74657.42
## 128 2003-08-01 X5.14 74712.46
## 129 2003-09-01 X5.14 74767.50
## 130 2003-10-01 X5.14 74684.74
## 131 2003-11-01 X5.14 74601.99
## 132 2003-12-01 X5.14 74577.43
## 133 2004-01-01 X5.14 74552.87
## 134 2004-02-01 X5.14 74699.82
## 135 2004-03-01 X5.14 74846.78
## 136 2004-04-01 X5.14 75181.64
## 137 2004-05-01 X5.14 75516.51
## 138 2004-06-01 X5.14 76070.38
## 139 2004-07-01 X5.14 76624.25
## 140 2004-08-01 X5.14 77257.43
## 141 2004-09-01 X5.14 77890.61
## 142 2004-10-01 X5.14 78245.85
## 143 2004-11-01 X5.14 78601.09
## 144 2004-12-01 X5.14 78664.65
## 145 2005-01-01 X5.14 78728.20
## 146 2005-02-01 X5.14 78626.38
## 147 2005-03-01 X5.14 78524.57
## 148 2005-04-01 X5.14 78259.11
## 149 2005-05-01 X5.14 77993.65
## 150 2005-06-01 X5.14 77454.33
## 151 2005-07-01 X5.14 76915.00
## 152 2005-08-01 X5.14 76423.36
## 153 2005-09-01 X5.14 75931.71
## 154 2005-10-01 X5.14 75840.81
## 155 2005-11-01 X5.14 75749.91
## 156 2005-12-01 X5.14 75726.89
## 157 2006-01-01 X5.14 75703.88
## 158 2006-02-01 X5.14 75650.79
## 159 2006-03-01 X5.14 75597.70
## 160 2006-04-01 X5.14 75663.07
## 161 2006-05-01 X5.14 75728.45
## 162 2006-06-01 X5.14 75634.76
## 163 2006-07-01 X5.14 75541.07
## 164 2006-08-01 X5.14 75024.43
## 165 2006-09-01 X5.14 74507.80
## 166 2006-10-01 X5.14 73938.91
## 167 2006-11-01 X5.14 73370.03
## 168 2006-12-01 X5.14 73141.90
## 169 2007-01-01 X5.14 72913.77
## 170 2007-02-01 X5.14 72861.86
## 171 2007-03-01 X5.14 72809.96
## 172 2007-04-01 X5.14 72795.76
## 173 2007-05-01 X5.14 72781.57
## 174 2007-06-01 X5.14 72985.12
## 175 2007-07-01 X5.14 73188.68
## 176 2007-08-01 X5.14 73667.17
## 177 2007-09-01 X5.14 74145.65
## 178 2007-10-01 X5.14 74572.80
## 179 2007-11-01 X5.14 74999.94
## 180 2007-12-01 X5.14 75259.78
## 181 2008-01-01 X5.14 75519.63
## 182 2008-02-01 X5.14 75781.47
## 183 2008-03-01 X5.14 76043.31
## 184 2008-04-01 X5.14 76358.20
## 185 2008-05-01 X5.14 76673.09
## 186 2008-06-01 X5.14 76819.91
## 187 2008-07-01 X5.14 76966.74
## 188 2008-08-01 X5.14 76833.15
## 189 2008-09-01 X5.14 76699.55
## 190 2008-10-01 X5.14 76461.00
## 191 2008-11-01 X5.14 76222.45
## 192 2008-12-01 X5.14 76103.09
## 193 2009-01-01 X5.14 75983.72
## 194 2009-02-01 X5.14 75904.57
## 195 2009-03-01 X5.14 75825.42
## 196 2009-04-01 X5.14 75944.62
## 197 2009-05-01 X5.14 76063.83
## 198 2009-06-01 X5.14 76356.56
## 199 2009-07-01 X5.14 76649.30
## 200 2009-08-01 X5.14 76852.47
## 201 2009-09-01 X5.14 77055.64
## 202 2009-10-01 X5.14 76970.99
## 203 2009-11-01 X5.14 76886.34
## 204 2009-12-01 X5.14 76684.87
## 205 2010-01-01 X5.14 76483.39
## 206 2010-02-01 X5.14 76294.82
## 207 2010-03-01 X5.14 76106.25
## 208 2010-04-01 X5.14 75955.56
## 209 2010-05-01 X5.14 75804.86
## 210 2010-06-01 X5.14 75573.66
## 211 2010-07-01 X5.14 75342.46
## 212 2010-08-01 X5.14 75214.30
## 213 2010-09-01 X5.14 75086.15
## 214 2010-10-01 X5.14 75222.56
## 215 2010-11-01 X5.14 75358.96
## 216 2010-12-01 X5.14 75632.18
## 217 2011-01-01 X5.14 75905.39
## 218 2011-02-01 X5.14 75904.98
## 219 2011-03-01 X5.14 75904.56
## 220 2011-04-01 X5.14 75836.73
## 221 2011-05-01 X5.14 75768.90
## 222 2011-06-01 X5.14 75761.54
## 223 2011-07-01 X5.14 75754.18
## 224 2011-08-01 X5.14 75721.76
## 225 2011-09-01 X5.14 75689.33
## 226 2011-10-01 X5.14 75488.86
## 227 2011-11-01 X5.14 75288.38
## 228 2011-12-01 X5.14 75258.35
## 229 2012-01-01 X5.14 75228.32
## 230 2012-02-01 X5.14 75504.05
## 231 2012-03-01 X5.14 75779.77
## 232 2012-04-01 X5.14 76160.33
## 233 2012-05-01 X5.14 76540.89
## 234 2012-06-01 X5.14 76911.27
## 235 2012-07-01 X5.14 77281.65
## 236 2012-08-01 X5.14 77780.56
## 237 2012-09-01 X5.14 78279.47
## 238 2012-10-01 X5.14 78620.29
## 239 2012-11-01 X5.14 78961.11
## 240 2012-12-01 X5.14 78969.64
## 241 2013-01-01 X5.14 78978.17
## 242 2013-02-01 X5.14 78717.58
## 243 2013-03-01 X5.14 78456.99
## 244 2013-04-01 X5.14 78034.48
## 245 2013-05-01 X5.14 77611.96
## 246 2013-06-01 X5.14 77304.55
## 247 2013-07-01 X5.14 76997.14
## 248 2013-08-01 X5.14 76741.51
## 249 2013-09-01 X5.14 76485.87
## 250 2013-10-01 X5.14 76260.03
## 251 2013-11-01 X5.14 76034.18
## 252 2013-12-01 X5.14 76019.14
## 253 2014-01-01 X5.14 76004.09
## 254 2014-02-01 X5.14 76138.81
## 255 2014-03-01 X5.14 76273.54
## 256 2014-04-01 X5.14 76478.59
## 257 2014-05-01 X5.14 76683.65
## 258 2014-06-01 X5.14 76767.84
## 259 2014-07-01 X5.14 76852.02
## 260 2014-08-01 X5.14 76604.88
## 261 2014-09-01 X5.14 76357.74
## 262 2014-10-01 X5.14 76171.29
## 263 2014-11-01 X5.14 75984.83
## 264 2014-12-01 X5.14 75923.36
## 265 2015-01-01 X5.14 75861.90
## 266 2015-02-01 X5.14 75848.72
## 267 2015-03-01 X5.14 75835.54
## 268 2015-04-01 X5.14 75815.88
## 269 2015-05-01 X5.14 75796.22
## 270 2015-06-01 X5.14 75908.62
## 271 2015-07-01 X5.14 76021.03
## 272 2015-08-01 X5.14 76342.91
## 273 2015-09-01 X5.14 76664.79
## 274 2015-10-01 X5.14 76916.72
## 275 2015-11-01 X5.14 77168.66
## 276 2015-12-01 X5.14 77120.41
## 277 2016-01-01 X5.14 77072.16
## 278 2016-02-01 X5.14 76933.01
## 279 2016-03-01 X5.14 76793.85
## 280 2016-04-01 X5.14 76613.20
## 281 2016-05-01 X5.14 76432.55
## 282 2016-06-01 X5.14 76256.71
## 283 2016-07-01 X5.14 76080.87
## 284 2016-08-01 X5.14 75929.87
## 285 2016-09-01 X5.14 75778.87
## 286 2016-10-01 X5.14 75698.99
## 287 2016-11-01 X5.14 75619.10
## 288 2016-12-01 X5.14 75610.06
## 289 2017-01-01 X5.14 75601.01
## 290 2017-02-01 X5.14 75522.64
## 291 2017-03-01 X5.14 75444.27
## 292 2017-04-01 X5.14 75254.71
## 293 2017-05-01 X5.14 75065.15
## 294 2017-06-01 X5.14 74896.92
## 295 2017-07-01 X5.14 74728.69
## 296 2017-08-01 X5.14 74541.31
## 297 2017-09-01 X5.14 74353.94
## 298 2017-10-01 X5.14 74145.30
## 299 2017-11-01 X5.14 73936.65
## 300 2017-12-01 X5.14 73717.80
## 301 1993-01-01 X5.66 89414.25
## 302 1993-02-01 X5.66 89410.58
## 303 1993-03-01 X5.66 89406.92
## 304 1993-04-01 X5.66 89384.82
## 305 1993-05-01 X5.66 89362.72
## 306 1993-06-01 X5.66 89334.77
## 307 1993-07-01 X5.66 89306.82
## 308 1993-08-01 X5.66 89273.57
## 309 1993-09-01 X5.66 89240.33
## 310 1993-10-01 X5.66 89073.17
## 311 1993-11-01 X5.66 88906.01
## 312 1993-12-01 X5.66 88652.77
## 313 1994-01-01 X5.66 88399.54
## 314 1994-02-01 X5.66 88184.36
## 315 1994-03-01 X5.66 87969.18
## 316 1994-04-01 X5.66 87715.14
## 317 1994-05-01 X5.66 87461.10
## 318 1994-06-01 X5.66 87284.28
## 319 1994-07-01 X5.66 87107.46
## 320 1994-08-01 X5.66 87144.84
## 321 1994-09-01 X5.66 87182.23
## 322 1994-10-01 X5.66 87385.17
## 323 1994-11-01 X5.66 87588.12
## 324 1994-12-01 X5.66 87729.44
## 325 1995-01-01 X5.66 87870.76
## 326 1995-02-01 X5.66 87993.54
## 327 1995-03-01 X5.66 88116.32
## 328 1995-04-01 X5.66 88310.27
## 329 1995-05-01 X5.66 88504.22
## 330 1995-06-01 X5.66 88818.42
## 331 1995-07-01 X5.66 89132.63
## 332 1995-08-01 X5.66 89434.03
## 333 1995-09-01 X5.66 89735.44
## 334 1995-10-01 X5.66 89879.51
## 335 1995-11-01 X5.66 90023.59
## 336 1995-12-01 X5.66 90050.75
## 337 1996-01-01 X5.66 90077.92
## 338 1996-02-01 X5.66 90004.67
## 339 1996-03-01 X5.66 89931.42
## 340 1996-04-01 X5.66 89860.44
## 341 1996-05-01 X5.66 89789.47
## 342 1996-06-01 X5.66 89723.93
## 343 1996-07-01 X5.66 89658.39
## 344 1996-08-01 X5.66 89375.33
## 345 1996-09-01 X5.66 89092.27
## 346 1996-10-01 X5.66 88805.39
## 347 1996-11-01 X5.66 88518.51
## 348 1996-12-01 X5.66 88404.88
## 349 1997-01-01 X5.66 88291.25
## 350 1997-02-01 X5.66 88164.67
## 351 1997-03-01 X5.66 88038.10
## 352 1997-04-01 X5.66 87617.59
## 353 1997-05-01 X5.66 87197.09
## 354 1997-06-01 X5.66 86875.95
## 355 1997-07-01 X5.66 86554.81
## 356 1997-08-01 X5.66 86793.91
## 357 1997-09-01 X5.66 87033.01
## 358 1997-10-01 X5.66 87269.68
## 359 1997-11-01 X5.66 87506.34
## 360 1997-12-01 X5.66 87451.25
## 361 1998-01-01 X5.66 87396.17
## 362 1998-02-01 X5.66 87328.10
## 363 1998-03-01 X5.66 87260.04
## 364 1998-04-01 X5.66 87105.66
## 365 1998-05-01 X5.66 86951.29
## 366 1998-06-01 X5.66 86850.42
## 367 1998-07-01 X5.66 86749.55
## 368 1998-08-01 X5.66 86807.19
## 369 1998-09-01 X5.66 86864.84
## 370 1998-10-01 X5.66 87125.75
## 371 1998-11-01 X5.66 87386.67
## 372 1998-12-01 X5.66 87675.10
## 373 1999-01-01 X5.66 87963.52
## 374 1999-02-01 X5.66 88376.45
## 375 1999-03-01 X5.66 88789.37
## 376 1999-04-01 X5.66 89146.22
## 377 1999-05-01 X5.66 89503.08
## 378 1999-06-01 X5.66 89509.17
## 379 1999-07-01 X5.66 89515.27
## 380 1999-08-01 X5.66 89387.79
## 381 1999-09-01 X5.66 89260.31
## 382 1999-10-01 X5.66 89339.87
## 383 1999-11-01 X5.66 89419.44
## 384 1999-12-01 X5.66 89447.04
## 385 2000-01-01 X5.66 89474.63
## 386 2000-02-01 X5.66 89484.13
## 387 2000-03-01 X5.66 89493.63
## 388 2000-04-01 X5.66 89653.24
## 389 2000-05-01 X5.66 89812.85
## 390 2000-06-01 X5.66 90056.91
## 391 2000-07-01 X5.66 90300.97
## 392 2000-08-01 X5.66 90396.51
## 393 2000-09-01 X5.66 90492.06
## 394 2000-10-01 X5.66 90454.50
## 395 2000-11-01 X5.66 90416.94
## 396 2000-12-01 X5.66 90334.72
## 397 2001-01-01 X5.66 90252.50
## 398 2001-02-01 X5.66 90034.80
## 399 2001-03-01 X5.66 89817.11
## 400 2001-04-01 X5.66 89564.20
## 401 2001-05-01 X5.66 89311.29
## 402 2001-06-01 X5.66 89126.65
## 403 2001-07-01 X5.66 88942.01
## 404 2001-08-01 X5.66 88926.35
## 405 2001-09-01 X5.66 88910.69
## 406 2001-10-01 X5.66 88951.95
## 407 2001-11-01 X5.66 88993.20
## 408 2001-12-01 X5.66 89017.18
## 409 2002-01-01 X5.66 89041.16
## 410 2002-02-01 X5.66 89038.14
## 411 2002-03-01 X5.66 89035.13
## 412 2002-04-01 X5.66 88874.98
## 413 2002-05-01 X5.66 88714.82
## 414 2002-06-01 X5.66 88305.79
## 415 2002-07-01 X5.66 87896.75
## 416 2002-08-01 X5.66 87437.31
## 417 2002-09-01 X5.66 86977.88
## 418 2002-10-01 X5.66 86737.34
## 419 2002-11-01 X5.66 86496.80
## 420 2002-12-01 X5.66 86461.14
## 421 2003-01-01 X5.66 86425.47
## 422 2003-02-01 X5.66 86453.99
## 423 2003-03-01 X5.66 86482.51
## 424 2003-04-01 X5.66 86651.44
## 425 2003-05-01 X5.66 86820.37
## 426 2003-06-01 X5.66 87017.38
## 427 2003-07-01 X5.66 87214.39
## 428 2003-08-01 X5.66 87306.15
## 429 2003-09-01 X5.66 87397.91
## 430 2003-10-01 X5.66 87367.11
## 431 2003-11-01 X5.66 87336.31
## 432 2003-12-01 X5.66 87348.58
## 433 2004-01-01 X5.66 87360.85
## 434 2004-02-01 X5.66 87519.27
## 435 2004-03-01 X5.66 87677.70
## 436 2004-04-01 X5.66 87985.16
## 437 2004-05-01 X5.66 88292.62
## 438 2004-06-01 X5.66 88788.20
## 439 2004-07-01 X5.66 89283.77
## 440 2004-08-01 X5.66 89853.62
## 441 2004-09-01 X5.66 90423.46
## 442 2004-10-01 X5.66 90743.14
## 443 2004-11-01 X5.66 91062.82
## 444 2004-12-01 X5.66 91108.13
## 445 2005-01-01 X5.66 91153.44
## 446 2005-02-01 X5.66 91057.18
## 447 2005-03-01 X5.66 90960.92
## 448 2005-04-01 X5.66 90724.04
## 449 2005-05-01 X5.66 90487.16
## 450 2005-06-01 X5.66 89971.85
## 451 2005-07-01 X5.66 89456.54
## 452 2005-08-01 X5.66 88964.93
## 453 2005-09-01 X5.66 88473.31
## 454 2005-10-01 X5.66 88360.27
## 455 2005-11-01 X5.66 88247.23
## 456 2005-12-01 X5.66 88208.80
## 457 2006-01-01 X5.66 88170.37
## 458 2006-02-01 X5.66 88108.40
## 459 2006-03-01 X5.66 88046.43
## 460 2006-04-01 X5.66 88085.29
## 461 2006-05-01 X5.66 88124.15
## 462 2006-06-01 X5.66 88028.44
## 463 2006-07-01 X5.66 87932.73
## 464 2006-08-01 X5.66 87472.66
## 465 2006-09-01 X5.66 87012.59
## 466 2006-10-01 X5.66 86499.87
## 467 2006-11-01 X5.66 85987.15
## 468 2006-12-01 X5.66 85775.83
## 469 2007-01-01 X5.66 85564.51
## 470 2007-02-01 X5.66 85521.03
## 471 2007-03-01 X5.66 85477.54
## 472 2007-04-01 X5.66 85465.66
## 473 2007-05-01 X5.66 85453.78
## 474 2007-06-01 X5.66 85636.27
## 475 2007-07-01 X5.66 85818.76
## 476 2007-08-01 X5.66 86247.68
## 477 2007-09-01 X5.66 86676.61
## 478 2007-10-01 X5.66 87084.53
## 479 2007-11-01 X5.66 87492.46
## 480 2007-12-01 X5.66 87747.93
## 481 2008-01-01 X5.66 88003.39
## 482 2008-02-01 X5.66 88251.50
## 483 2008-03-01 X5.66 88499.60
## 484 2008-04-01 X5.66 88798.25
## 485 2008-05-01 X5.66 89096.90
## 486 2008-06-01 X5.66 89216.72
## 487 2008-07-01 X5.66 89336.54
## 488 2008-08-01 X5.66 89191.88
## 489 2008-09-01 X5.66 89047.22
## 490 2008-10-01 X5.66 88829.70
## 491 2008-11-01 X5.66 88612.18
## 492 2008-12-01 X5.66 88512.47
## 493 2009-01-01 X5.66 88412.76
## 494 2009-02-01 X5.66 88345.32
## 495 2009-03-01 X5.66 88277.88
## 496 2009-04-01 X5.66 88406.27
## 497 2009-05-01 X5.66 88534.65
## 498 2009-06-01 X5.66 88857.20
## 499 2009-07-01 X5.66 89179.74
## 500 2009-08-01 X5.66 89419.79
## 501 2009-09-01 X5.66 89659.84
## 502 2009-10-01 X5.66 89601.10
## 503 2009-11-01 X5.66 89542.35
## 504 2009-12-01 X5.66 89375.43
## 505 2010-01-01 X5.66 89208.51
## 506 2010-02-01 X5.66 89048.10
## 507 2010-03-01 X5.66 88887.68
## 508 2010-04-01 X5.66 88730.75
## 509 2010-05-01 X5.66 88573.82
## 510 2010-06-01 X5.66 88304.75
## 511 2010-07-01 X5.66 88035.69
## 512 2010-08-01 X5.66 87890.08
## 513 2010-09-01 X5.66 87744.47
## 514 2010-10-01 X5.66 87864.04
## 515 2010-11-01 X5.66 87983.61
## 516 2010-12-01 X5.66 88231.61
## 517 2011-01-01 X5.66 88479.60
## 518 2011-02-01 X5.66 88473.93
## 519 2011-03-01 X5.66 88468.26
## 520 2011-04-01 X5.66 88422.74
## 521 2011-05-01 X5.66 88377.21
## 522 2011-06-01 X5.66 88378.62
## 523 2011-07-01 X5.66 88380.02
## 524 2011-08-01 X5.66 88315.13
## 525 2011-09-01 X5.66 88250.24
## 526 2011-10-01 X5.66 88025.26
## 527 2011-11-01 X5.66 87800.28
## 528 2011-12-01 X5.66 87757.29
## 529 2012-01-01 X5.66 87714.30
## 530 2012-02-01 X5.66 87953.41
## 531 2012-03-01 X5.66 88192.52
## 532 2012-04-01 X5.66 88548.42
## 533 2012-05-01 X5.66 88904.32
## 534 2012-06-01 X5.66 89301.33
## 535 2012-07-01 X5.66 89698.33
## 536 2012-08-01 X5.66 90232.41
## 537 2012-09-01 X5.66 90766.50
## 538 2012-10-01 X5.66 91126.36
## 539 2012-11-01 X5.66 91486.22
## 540 2012-12-01 X5.66 91512.51
## 541 2013-01-01 X5.66 91538.80
## 542 2013-02-01 X5.66 91301.63
## 543 2013-03-01 X5.66 91064.46
## 544 2013-04-01 X5.66 90635.26
## 545 2013-05-01 X5.66 90206.06
## 546 2013-06-01 X5.66 89842.86
## 547 2013-07-01 X5.66 89479.65
## 548 2013-08-01 X5.66 89171.67
## 549 2013-09-01 X5.66 88863.69
## 550 2013-10-01 X5.66 88599.87
## 551 2013-11-01 X5.66 88336.05
## 552 2013-12-01 X5.66 88289.00
## 553 2014-01-01 X5.66 88241.95
## 554 2014-02-01 X5.66 88357.78
## 555 2014-03-01 X5.66 88473.62
## 556 2014-04-01 X5.66 88695.53
## 557 2014-05-01 X5.66 88917.44
## 558 2014-06-01 X5.66 89046.57
## 559 2014-07-01 X5.66 89175.69
## 560 2014-08-01 X5.66 88964.88
## 561 2014-09-01 X5.66 88754.07
## 562 2014-10-01 X5.66 88583.34
## 563 2014-11-01 X5.66 88412.61
## 564 2014-12-01 X5.66 88370.02
## 565 2015-01-01 X5.66 88327.44
## 566 2015-02-01 X5.66 88325.16
## 567 2015-03-01 X5.66 88322.87
## 568 2015-04-01 X5.66 88310.98
## 569 2015-05-01 X5.66 88299.08
## 570 2015-06-01 X5.66 88401.25
## 571 2015-07-01 X5.66 88503.43
## 572 2015-08-01 X5.66 88807.10
## 573 2015-09-01 X5.66 89110.78
## 574 2015-10-01 X5.66 89320.44
## 575 2015-11-01 X5.66 89530.10
## 576 2015-12-01 X5.66 89435.03
## 577 2016-01-01 X5.66 89339.96
## 578 2016-02-01 X5.66 89179.85
## 579 2016-03-01 X5.66 89019.74
## 580 2016-04-01 X5.66 88860.38
## 581 2016-05-01 X5.66 88701.01
## 582 2016-06-01 X5.66 88540.88
## 583 2016-07-01 X5.66 88380.75
## 584 2016-08-01 X5.66 88221.53
## 585 2016-09-01 X5.66 88062.30
## 586 2016-10-01 X5.66 87990.50
## 587 2016-11-01 X5.66 87918.69
## 588 2016-12-01 X5.66 87945.46
## 589 2017-01-01 X5.66 87972.22
## 590 2017-02-01 X5.66 87917.27
## 591 2017-03-01 X5.66 87862.31
## 592 2017-04-01 X5.66 87692.90
## 593 2017-05-01 X5.66 87523.48
## 594 2017-06-01 X5.66 87357.82
## 595 2017-07-01 X5.66 87192.17
## 596 2017-08-01 X5.66 87011.17
## 597 2017-09-01 X5.66 86830.16
## 598 2017-10-01 X5.66 86627.58
## 599 2017-11-01 X5.66 86424.99
## 600 2017-12-01 X5.66 86207.49
## 601 1993-01-01 X6.17 102388.62
## 602 1993-02-01 X6.17 102499.50
## 603 1993-03-01 X6.17 102610.39
## 604 1993-04-01 X6.17 102702.91
## 605 1993-05-01 X6.17 102795.43
## 606 1993-06-01 X6.17 102882.05
## 607 1993-07-01 X6.17 102968.66
## 608 1993-08-01 X6.17 103045.11
## 609 1993-09-01 X6.17 103121.55
## 610 1993-10-01 X6.17 103021.81
## 611 1993-11-01 X6.17 102922.07
## 612 1993-12-01 X6.17 102691.69
## 613 1994-01-01 X6.17 102461.30
## 614 1994-02-01 X6.17 102264.64
## 615 1994-03-01 X6.17 102067.99
## 616 1994-04-01 X6.17 101786.09
## 617 1994-05-01 X6.17 101504.19
## 618 1994-06-01 X6.17 101269.01
## 619 1994-07-01 X6.17 101033.83
## 620 1994-08-01 X6.17 101015.65
## 621 1994-09-01 X6.17 100997.47
## 622 1994-10-01 X6.17 101169.20
## 623 1994-11-01 X6.17 101340.93
## 624 1994-12-01 X6.17 101465.75
## 625 1995-01-01 X6.17 101590.57
## 626 1995-02-01 X6.17 101717.27
## 627 1995-03-01 X6.17 101843.97
## 628 1995-04-01 X6.17 102042.63
## 629 1995-05-01 X6.17 102241.29
## 630 1995-06-01 X6.17 102510.01
## 631 1995-07-01 X6.17 102778.72
## 632 1995-08-01 X6.17 103034.20
## 633 1995-09-01 X6.17 103289.67
## 634 1995-10-01 X6.17 103415.14
## 635 1995-11-01 X6.17 103540.60
## 636 1995-12-01 X6.17 103557.90
## 637 1996-01-01 X6.17 103575.20
## 638 1996-02-01 X6.17 103500.18
## 639 1996-03-01 X6.17 103425.16
## 640 1996-04-01 X6.17 103367.69
## 641 1996-05-01 X6.17 103310.23
## 642 1996-06-01 X6.17 103270.65
## 643 1996-07-01 X6.17 103231.08
## 644 1996-08-01 X6.17 102940.47
## 645 1996-09-01 X6.17 102649.86
## 646 1996-10-01 X6.17 102312.49
## 647 1996-11-01 X6.17 101975.13
## 648 1996-12-01 X6.17 101826.53
## 649 1997-01-01 X6.17 101677.93
## 650 1997-02-01 X6.17 101543.55
## 651 1997-03-01 X6.17 101409.18
## 652 1997-04-01 X6.17 101034.13
## 653 1997-05-01 X6.17 100659.08
## 654 1997-06-01 X6.17 100432.44
## 655 1997-07-01 X6.17 100205.79
## 656 1997-08-01 X6.17 100512.84
## 657 1997-09-01 X6.17 100819.88
## 658 1997-10-01 X6.17 101072.88
## 659 1997-11-01 X6.17 101325.88
## 660 1997-12-01 X6.17 101274.76
## 661 1998-01-01 X6.17 101223.63
## 662 1998-02-01 X6.17 101161.04
## 663 1998-03-01 X6.17 101098.45
## 664 1998-04-01 X6.17 100913.15
## 665 1998-05-01 X6.17 100727.85
## 666 1998-06-01 X6.17 100560.85
## 667 1998-07-01 X6.17 100393.85
## 668 1998-08-01 X6.17 100409.69
## 669 1998-09-01 X6.17 100425.52
## 670 1998-10-01 X6.17 100662.44
## 671 1998-11-01 X6.17 100899.35
## 672 1998-12-01 X6.17 101173.59
## 673 1999-01-01 X6.17 101447.83
## 674 1999-02-01 X6.17 101881.75
## 675 1999-03-01 X6.17 102315.66
## 676 1999-04-01 X6.17 102711.49
## 677 1999-05-01 X6.17 103107.32
## 678 1999-06-01 X6.17 103158.10
## 679 1999-07-01 X6.17 103208.89
## 680 1999-08-01 X6.17 103120.09
## 681 1999-09-01 X6.17 103031.29
## 682 1999-10-01 X6.17 103125.04
## 683 1999-11-01 X6.17 103218.78
## 684 1999-12-01 X6.17 103216.75
## 685 2000-01-01 X6.17 103214.73
## 686 2000-02-01 X6.17 103188.37
## 687 2000-03-01 X6.17 103162.01
## 688 2000-04-01 X6.17 103298.47
## 689 2000-05-01 X6.17 103434.93
## 690 2000-06-01 X6.17 103636.73
## 691 2000-07-01 X6.17 103838.54
## 692 2000-08-01 X6.17 103880.80
## 693 2000-09-01 X6.17 103923.05
## 694 2000-10-01 X6.17 103869.76
## 695 2000-11-01 X6.17 103816.47
## 696 2000-12-01 X6.17 103746.69
## 697 2001-01-01 X6.17 103676.91
## 698 2001-02-01 X6.17 103480.90
## 699 2001-03-01 X6.17 103284.90
## 700 2001-04-01 X6.17 103081.59
## 701 2001-05-01 X6.17 102878.28
## 702 2001-06-01 X6.17 102765.92
## 703 2001-07-01 X6.17 102653.57
## 704 2001-08-01 X6.17 102686.87
## 705 2001-09-01 X6.17 102720.17
## 706 2001-10-01 X6.17 102775.25
## 707 2001-11-01 X6.17 102830.33
## 708 2001-12-01 X6.17 102856.41
## 709 2002-01-01 X6.17 102882.49
## 710 2002-02-01 X6.17 102874.16
## 711 2002-03-01 X6.17 102865.84
## 712 2002-04-01 X6.17 102717.72
## 713 2002-05-01 X6.17 102569.59
## 714 2002-06-01 X6.17 102188.63
## 715 2002-07-01 X6.17 101807.67
## 716 2002-08-01 X6.17 101366.86
## 717 2002-09-01 X6.17 100926.05
## 718 2002-10-01 X6.17 100694.22
## 719 2002-11-01 X6.17 100462.40
## 720 2002-12-01 X6.17 100420.89
## 721 2003-01-01 X6.17 100379.37
## 722 2003-02-01 X6.17 100383.17
## 723 2003-03-01 X6.17 100386.97
## 724 2003-04-01 X6.17 100511.12
## 725 2003-05-01 X6.17 100635.28
## 726 2003-06-01 X6.17 100765.82
## 727 2003-07-01 X6.17 100896.36
## 728 2003-08-01 X6.17 100922.66
## 729 2003-09-01 X6.17 100948.96
## 730 2003-10-01 X6.17 100887.75
## 731 2003-11-01 X6.17 100826.54
## 732 2003-12-01 X6.17 100826.92
## 733 2004-01-01 X6.17 100827.29
## 734 2004-02-01 X6.17 100984.02
## 735 2004-03-01 X6.17 101140.75
## 736 2004-04-01 X6.17 101464.16
## 737 2004-05-01 X6.17 101787.56
## 738 2004-06-01 X6.17 102299.28
## 739 2004-07-01 X6.17 102810.99
## 740 2004-08-01 X6.17 103395.13
## 741 2004-09-01 X6.17 103979.27
## 742 2004-10-01 X6.17 104297.78
## 743 2004-11-01 X6.17 104616.30
## 744 2004-12-01 X6.17 104668.31
## 745 2005-01-01 X6.17 104720.32
## 746 2005-02-01 X6.17 104627.90
## 747 2005-03-01 X6.17 104535.48
## 748 2005-04-01 X6.17 104292.49
## 749 2005-05-01 X6.17 104049.51
## 750 2005-06-01 X6.17 103541.58
## 751 2005-07-01 X6.17 103033.64
## 752 2005-08-01 X6.17 102558.41
## 753 2005-09-01 X6.17 102083.17
## 754 2005-10-01 X6.17 101982.45
## 755 2005-11-01 X6.17 101881.73
## 756 2005-12-01 X6.17 101855.13
## 757 2006-01-01 X6.17 101828.52
## 758 2006-02-01 X6.17 101803.52
## 759 2006-03-01 X6.17 101778.53
## 760 2006-04-01 X6.17 101892.49
## 761 2006-05-01 X6.17 102006.45
## 762 2006-06-01 X6.17 101968.31
## 763 2006-07-01 X6.17 101930.18
## 764 2006-08-01 X6.17 101467.53
## 765 2006-09-01 X6.17 101004.89
## 766 2006-10-01 X6.17 100479.49
## 767 2006-11-01 X6.17 99954.10
## 768 2006-12-01 X6.17 99722.92
## 769 2007-01-01 X6.17 99491.74
## 770 2007-02-01 X6.17 99404.88
## 771 2007-03-01 X6.17 99318.03
## 772 2007-04-01 X6.17 99267.40
## 773 2007-05-01 X6.17 99216.77
## 774 2007-06-01 X6.17 99381.84
## 775 2007-07-01 X6.17 99546.91
## 776 2007-08-01 X6.17 99986.53
## 777 2007-09-01 X6.17 100426.14
## 778 2007-10-01 X6.17 100839.02
## 779 2007-11-01 X6.17 101251.90
## 780 2007-12-01 X6.17 101515.97
## 781 2008-01-01 X6.17 101780.04
## 782 2008-02-01 X6.17 102037.04
## 783 2008-03-01 X6.17 102294.04
## 784 2008-04-01 X6.17 102579.61
## 785 2008-05-01 X6.17 102865.18
## 786 2008-06-01 X6.17 102988.74
## 787 2008-07-01 X6.17 103112.31
## 788 2008-08-01 X6.17 102981.83
## 789 2008-09-01 X6.17 102851.36
## 790 2008-10-01 X6.17 102618.27
## 791 2008-11-01 X6.17 102385.18
## 792 2008-12-01 X6.17 102264.92
## 793 2009-01-01 X6.17 102144.66
## 794 2009-02-01 X6.17 102065.67
## 795 2009-03-01 X6.17 101986.69
## 796 2009-04-01 X6.17 102143.06
## 797 2009-05-01 X6.17 102299.42
## 798 2009-06-01 X6.17 102652.39
## 799 2009-07-01 X6.17 103005.36
## 800 2009-08-01 X6.17 103267.80
## 801 2009-09-01 X6.17 103530.24
## 802 2009-10-01 X6.17 103507.64
## 803 2009-11-01 X6.17 103485.04
## 804 2009-12-01 X6.17 103342.16
## 805 2010-01-01 X6.17 103199.28
## 806 2010-02-01 X6.17 103021.29
## 807 2010-03-01 X6.17 102843.30
## 808 2010-04-01 X6.17 102657.92
## 809 2010-05-01 X6.17 102472.55
## 810 2010-06-01 X6.17 102194.48
## 811 2010-07-01 X6.17 101916.41
## 812 2010-08-01 X6.17 101761.87
## 813 2010-09-01 X6.17 101607.32
## 814 2010-10-01 X6.17 101711.48
## 815 2010-11-01 X6.17 101815.64
## 816 2010-12-01 X6.17 102058.21
## 817 2011-01-01 X6.17 102300.78
## 818 2011-02-01 X6.17 102292.24
## 819 2011-03-01 X6.17 102283.69
## 820 2011-04-01 X6.17 102198.60
## 821 2011-05-01 X6.17 102113.50
## 822 2011-06-01 X6.17 102066.22
## 823 2011-07-01 X6.17 102018.94
## 824 2011-08-01 X6.17 101956.11
## 825 2011-09-01 X6.17 101893.29
## 826 2011-10-01 X6.17 101688.81
## 827 2011-11-01 X6.17 101484.34
## 828 2011-12-01 X6.17 101452.70
## 829 2012-01-01 X6.17 101421.05
## 830 2012-02-01 X6.17 101673.60
## 831 2012-03-01 X6.17 101926.15
## 832 2012-04-01 X6.17 102291.42
## 833 2012-05-01 X6.17 102656.68
## 834 2012-06-01 X6.17 103032.24
## 835 2012-07-01 X6.17 103407.80
## 836 2012-08-01 X6.17 103930.58
## 837 2012-09-01 X6.17 104453.36
## 838 2012-10-01 X6.17 104809.29
## 839 2012-11-01 X6.17 105165.22
## 840 2012-12-01 X6.17 105191.18
## 841 2013-01-01 X6.17 105217.13
## 842 2013-02-01 X6.17 104981.95
## 843 2013-03-01 X6.17 104746.77
## 844 2013-04-01 X6.17 104344.57
## 845 2013-05-01 X6.17 103942.37
## 846 2013-06-01 X6.17 103659.77
## 847 2013-07-01 X6.17 103377.18
## 848 2013-08-01 X6.17 103138.64
## 849 2013-09-01 X6.17 102900.11
## 850 2013-10-01 X6.17 102668.22
## 851 2013-11-01 X6.17 102436.33
## 852 2013-12-01 X6.17 102406.27
## 853 2014-01-01 X6.17 102376.21
## 854 2014-02-01 X6.17 102497.76
## 855 2014-03-01 X6.17 102619.31
## 856 2014-04-01 X6.17 102806.89
## 857 2014-05-01 X6.17 102994.46
## 858 2014-06-01 X6.17 103051.75
## 859 2014-07-01 X6.17 103109.04
## 860 2014-08-01 X6.17 102887.13
## 861 2014-09-01 X6.17 102665.22
## 862 2014-10-01 X6.17 102546.15
## 863 2014-11-01 X6.17 102427.07
## 864 2014-12-01 X6.17 102402.16
## 865 2015-01-01 X6.17 102377.24
## 866 2015-02-01 X6.17 102357.41
## 867 2015-03-01 X6.17 102337.58
## 868 2015-04-01 X6.17 102328.67
## 869 2015-05-01 X6.17 102319.77
## 870 2015-06-01 X6.17 102434.23
## 871 2015-07-01 X6.17 102548.69
## 872 2015-08-01 X6.17 102843.49
## 873 2015-09-01 X6.17 103138.29
## 874 2015-10-01 X6.17 103362.28
## 875 2015-11-01 X6.17 103586.27
## 876 2015-12-01 X6.17 103540.38
## 877 2016-01-01 X6.17 103494.48
## 878 2016-02-01 X6.17 103352.13
## 879 2016-03-01 X6.17 103209.78
## 880 2016-04-01 X6.17 102980.32
## 881 2016-05-01 X6.17 102750.85
## 882 2016-06-01 X6.17 102514.33
## 883 2016-07-01 X6.17 102277.80
## 884 2016-08-01 X6.17 102103.74
## 885 2016-09-01 X6.17 101929.67
## 886 2016-10-01 X6.17 101855.94
## 887 2016-11-01 X6.17 101782.21
## 888 2016-12-01 X6.17 101791.71
## 889 2017-01-01 X6.17 101801.21
## 890 2017-02-01 X6.17 101751.30
## 891 2017-03-01 X6.17 101701.40
## 892 2017-04-01 X6.17 101510.48
## 893 2017-05-01 X6.17 101319.56
## 894 2017-06-01 X6.17 101122.06
## 895 2017-07-01 X6.17 100924.55
## 896 2017-08-01 X6.17 100700.56
## 897 2017-09-01 X6.17 100476.58
## 898 2017-10-01 X6.17 100223.49
## 899 2017-11-01 X6.17 99970.39
## 900 2017-12-01 X6.17 99698.47
## 901 1993-01-01 X6.69 118223.57
## 902 1993-02-01 X6.69 118350.04
## 903 1993-03-01 X6.69 118476.51
## 904 1993-04-01 X6.69 118578.98
## 905 1993-05-01 X6.69 118681.46
## 906 1993-06-01 X6.69 118776.39
## 907 1993-07-01 X6.69 118871.31
## 908 1993-08-01 X6.69 118963.18
## 909 1993-09-01 X6.69 119055.05
## 910 1993-10-01 X6.69 118971.90
## 911 1993-11-01 X6.69 118888.75
## 912 1993-12-01 X6.69 118650.74
## 913 1994-01-01 X6.69 118412.74
## 914 1994-02-01 X6.69 118201.22
## 915 1994-03-01 X6.69 117989.70
## 916 1994-04-01 X6.69 117718.64
## 917 1994-05-01 X6.69 117447.59
## 918 1994-06-01 X6.69 117278.22
## 919 1994-07-01 X6.69 117108.85
## 920 1994-08-01 X6.69 117189.60
## 921 1994-09-01 X6.69 117270.35
## 922 1994-10-01 X6.69 117510.46
## 923 1994-11-01 X6.69 117750.56
## 924 1994-12-01 X6.69 117910.18
## 925 1995-01-01 X6.69 118069.80
## 926 1995-02-01 X6.69 118180.89
## 927 1995-03-01 X6.69 118291.98
## 928 1995-04-01 X6.69 118420.35
## 929 1995-05-01 X6.69 118548.72
## 930 1995-06-01 X6.69 118717.30
## 931 1995-07-01 X6.69 118885.89
## 932 1995-08-01 X6.69 119036.89
## 933 1995-09-01 X6.69 119187.89
## 934 1995-10-01 X6.69 119257.57
## 935 1995-11-01 X6.69 119327.24
## 936 1995-12-01 X6.69 119349.71
## 937 1996-01-01 X6.69 119372.17
## 938 1996-02-01 X6.69 119322.09
## 939 1996-03-01 X6.69 119272.01
## 940 1996-04-01 X6.69 119263.00
## 941 1996-05-01 X6.69 119253.99
## 942 1996-06-01 X6.69 119279.19
## 943 1996-07-01 X6.69 119304.39
## 944 1996-08-01 X6.69 119058.83
## 945 1996-09-01 X6.69 118813.26
## 946 1996-10-01 X6.69 118503.88
## 947 1996-11-01 X6.69 118194.51
## 948 1996-12-01 X6.69 118062.52
## 949 1997-01-01 X6.69 117930.53
## 950 1997-02-01 X6.69 117804.69
## 951 1997-03-01 X6.69 117678.85
## 952 1997-04-01 X6.69 117281.78
## 953 1997-05-01 X6.69 116884.72
## 954 1997-06-01 X6.69 116614.80
## 955 1997-07-01 X6.69 116344.89
## 956 1997-08-01 X6.69 116621.25
## 957 1997-09-01 X6.69 116897.62
## 958 1997-10-01 X6.69 117127.18
## 959 1997-11-01 X6.69 117356.73
## 960 1997-12-01 X6.69 117277.99
## 961 1998-01-01 X6.69 117199.24
## 962 1998-02-01 X6.69 117084.64
## 963 1998-03-01 X6.69 116970.05
## 964 1998-04-01 X6.69 116736.81
## 965 1998-05-01 X6.69 116503.57
## 966 1998-06-01 X6.69 116350.99
## 967 1998-07-01 X6.69 116198.41
## 968 1998-08-01 X6.69 116254.48
## 969 1998-09-01 X6.69 116310.56
## 970 1998-10-01 X6.69 116569.26
## 971 1998-11-01 X6.69 116827.97
## 972 1998-12-01 X6.69 117119.00
## 973 1999-01-01 X6.69 117410.04
## 974 1999-02-01 X6.69 117847.46
## 975 1999-03-01 X6.69 118284.87
## 976 1999-04-01 X6.69 118651.92
## 977 1999-05-01 X6.69 119018.97
## 978 1999-06-01 X6.69 119003.76
## 979 1999-07-01 X6.69 118988.55
## 980 1999-08-01 X6.69 118835.11
## 981 1999-09-01 X6.69 118681.67
## 982 1999-10-01 X6.69 118752.32
## 983 1999-11-01 X6.69 118822.96
## 984 1999-12-01 X6.69 118832.11
## 985 2000-01-01 X6.69 118841.26
## 986 2000-02-01 X6.69 118833.18
## 987 2000-03-01 X6.69 118825.10
## 988 2000-04-01 X6.69 119015.59
## 989 2000-05-01 X6.69 119206.08
## 990 2000-06-01 X6.69 119526.58
## 991 2000-07-01 X6.69 119847.07
## 992 2000-08-01 X6.69 120000.30
## 993 2000-09-01 X6.69 120153.54
## 994 2000-10-01 X6.69 120147.22
## 995 2000-11-01 X6.69 120140.89
## 996 2000-12-01 X6.69 120084.80
## 997 2001-01-01 X6.69 120028.71
## 998 2001-02-01 X6.69 119811.08
## 999 2001-03-01 X6.69 119593.45
## 1000 2001-04-01 X6.69 119292.92
## 1001 2001-05-01 X6.69 118992.39
## 1002 2001-06-01 X6.69 118719.17
## 1003 2001-07-01 X6.69 118445.94
## 1004 2001-08-01 X6.69 118365.86
## 1005 2001-09-01 X6.69 118285.78
## 1006 2001-10-01 X6.69 118308.30
## 1007 2001-11-01 X6.69 118330.83
## 1008 2001-12-01 X6.69 118369.14
## 1009 2002-01-01 X6.69 118407.46
## 1010 2002-02-01 X6.69 118439.88
## 1011 2002-03-01 X6.69 118472.30
## 1012 2002-04-01 X6.69 118395.21
## 1013 2002-05-01 X6.69 118318.12
## 1014 2002-06-01 X6.69 118013.31
## 1015 2002-07-01 X6.69 117708.51
## 1016 2002-08-01 X6.69 117308.62
## 1017 2002-09-01 X6.69 116908.73
## 1018 2002-10-01 X6.69 116666.55
## 1019 2002-11-01 X6.69 116424.37
## 1020 2002-12-01 X6.69 116374.28
## 1021 2003-01-01 X6.69 116324.20
## 1022 2003-02-01 X6.69 116326.20
## 1023 2003-03-01 X6.69 116328.19
## 1024 2003-04-01 X6.69 116439.51
## 1025 2003-05-01 X6.69 116550.83
## 1026 2003-06-01 X6.69 116672.71
## 1027 2003-07-01 X6.69 116794.60
## 1028 2003-08-01 X6.69 116824.64
## 1029 2003-09-01 X6.69 116854.69
## 1030 2003-10-01 X6.69 116792.16
## 1031 2003-11-01 X6.69 116729.63
## 1032 2003-12-01 X6.69 116727.50
## 1033 2004-01-01 X6.69 116725.38
## 1034 2004-02-01 X6.69 116882.01
## 1035 2004-03-01 X6.69 117038.65
## 1036 2004-04-01 X6.69 117354.57
## 1037 2004-05-01 X6.69 117670.50
## 1038 2004-06-01 X6.69 118186.39
## 1039 2004-07-01 X6.69 118702.29
## 1040 2004-08-01 X6.69 119285.45
## 1041 2004-09-01 X6.69 119868.61
## 1042 2004-10-01 X6.69 120180.79
## 1043 2004-11-01 X6.69 120492.98
## 1044 2004-12-01 X6.69 120532.70
## 1045 2005-01-01 X6.69 120572.42
## 1046 2005-02-01 X6.69 120514.33
## 1047 2005-03-01 X6.69 120456.25
## 1048 2005-04-01 X6.69 120306.99
## 1049 2005-05-01 X6.69 120157.74
## 1050 2005-06-01 X6.69 119681.73
## 1051 2005-07-01 X6.69 119205.71
## 1052 2005-08-01 X6.69 118702.40
## 1053 2005-09-01 X6.69 118199.08
## 1054 2005-10-01 X6.69 118060.16
## 1055 2005-11-01 X6.69 117921.23
## 1056 2005-12-01 X6.69 117854.13
## 1057 2006-01-01 X6.69 117787.02
## 1058 2006-02-01 X6.69 117679.79
## 1059 2006-03-01 X6.69 117572.56
## 1060 2006-04-01 X6.69 117579.72
## 1061 2006-05-01 X6.69 117586.89
## 1062 2006-06-01 X6.69 117527.74
## 1063 2006-07-01 X6.69 117468.60
## 1064 2006-08-01 X6.69 117029.06
## 1065 2006-09-01 X6.69 116589.52
## 1066 2006-10-01 X6.69 116085.33
## 1067 2006-11-01 X6.69 115581.15
## 1068 2006-12-01 X6.69 115369.71
## 1069 2007-01-01 X6.69 115158.27
## 1070 2007-02-01 X6.69 115098.35
## 1071 2007-03-01 X6.69 115038.43
## 1072 2007-04-01 X6.69 115022.01
## 1073 2007-05-01 X6.69 115005.58
## 1074 2007-06-01 X6.69 115217.01
## 1075 2007-07-01 X6.69 115428.43
## 1076 2007-08-01 X6.69 115928.89
## 1077 2007-09-01 X6.69 116429.34
## 1078 2007-10-01 X6.69 116887.13
## 1079 2007-11-01 X6.69 117344.91
## 1080 2007-12-01 X6.69 117630.30
## 1081 2008-01-01 X6.69 117915.68
## 1082 2008-02-01 X6.69 118198.32
## 1083 2008-03-01 X6.69 118480.96
## 1084 2008-04-01 X6.69 118780.70
## 1085 2008-05-01 X6.69 119080.44
## 1086 2008-06-01 X6.69 119167.51
## 1087 2008-07-01 X6.69 119254.58
## 1088 2008-08-01 X6.69 119080.87
## 1089 2008-09-01 X6.69 118907.16
## 1090 2008-10-01 X6.69 118659.52
## 1091 2008-11-01 X6.69 118411.89
## 1092 2008-12-01 X6.69 118298.06
## 1093 2009-01-01 X6.69 118184.23
## 1094 2009-02-01 X6.69 118096.43
## 1095 2009-03-01 X6.69 118008.63
## 1096 2009-04-01 X6.69 118136.09
## 1097 2009-05-01 X6.69 118263.54
## 1098 2009-06-01 X6.69 118585.93
## 1099 2009-07-01 X6.69 118908.32
## 1100 2009-08-01 X6.69 119165.76
## 1101 2009-09-01 X6.69 119423.20
## 1102 2009-10-01 X6.69 119385.39
## 1103 2009-11-01 X6.69 119347.58
## 1104 2009-12-01 X6.69 119188.56
## 1105 2010-01-01 X6.69 119029.54
## 1106 2010-02-01 X6.69 118870.69
## 1107 2010-03-01 X6.69 118711.83
## 1108 2010-04-01 X6.69 118556.75
## 1109 2010-05-01 X6.69 118401.66
## 1110 2010-06-01 X6.69 118130.57
## 1111 2010-07-01 X6.69 117859.49
## 1112 2010-08-01 X6.69 117682.70
## 1113 2010-09-01 X6.69 117505.92
## 1114 2010-10-01 X6.69 117584.43
## 1115 2010-11-01 X6.69 117662.95
## 1116 2010-12-01 X6.69 117879.18
## 1117 2011-01-01 X6.69 118095.40
## 1118 2011-02-01 X6.69 118074.22
## 1119 2011-03-01 X6.69 118053.03
## 1120 2011-04-01 X6.69 117965.61
## 1121 2011-05-01 X6.69 117878.19
## 1122 2011-06-01 X6.69 117818.06
## 1123 2011-07-01 X6.69 117757.93
## 1124 2011-08-01 X6.69 117698.35
## 1125 2011-09-01 X6.69 117638.77
## 1126 2011-10-01 X6.69 117469.77
## 1127 2011-11-01 X6.69 117300.76
## 1128 2011-12-01 X6.69 117288.30
## 1129 2012-01-01 X6.69 117275.84
## 1130 2012-02-01 X6.69 117532.21
## 1131 2012-03-01 X6.69 117788.58
## 1132 2012-04-01 X6.69 118179.74
## 1133 2012-05-01 X6.69 118570.89
## 1134 2012-06-01 X6.69 118996.08
## 1135 2012-07-01 X6.69 119421.26
## 1136 2012-08-01 X6.69 119992.77
## 1137 2012-09-01 X6.69 120564.28
## 1138 2012-10-01 X6.69 120947.60
## 1139 2012-11-01 X6.69 121330.93
## 1140 2012-12-01 X6.69 121363.91
## 1141 2013-01-01 X6.69 121396.88
## 1142 2013-02-01 X6.69 121155.07
## 1143 2013-03-01 X6.69 120913.26
## 1144 2013-04-01 X6.69 120481.95
## 1145 2013-05-01 X6.69 120050.65
## 1146 2013-06-01 X6.69 119698.74
## 1147 2013-07-01 X6.69 119346.84
## 1148 2013-08-01 X6.69 119033.57
## 1149 2013-09-01 X6.69 118720.31
## 1150 2013-10-01 X6.69 118449.13
## 1151 2013-11-01 X6.69 118177.95
## 1152 2013-12-01 X6.69 118140.30
## 1153 2014-01-01 X6.69 118102.64
## 1154 2014-02-01 X6.69 118220.76
## 1155 2014-03-01 X6.69 118338.88
## 1156 2014-04-01 X6.69 118529.46
## 1157 2014-05-01 X6.69 118720.04
## 1158 2014-06-01 X6.69 118802.45
## 1159 2014-07-01 X6.69 118884.85
## 1160 2014-08-01 X6.69 118679.50
## 1161 2014-09-01 X6.69 118474.15
## 1162 2014-10-01 X6.69 118361.11
## 1163 2014-11-01 X6.69 118248.08
## 1164 2014-12-01 X6.69 118245.06
## 1165 2015-01-01 X6.69 118242.05
## 1166 2015-02-01 X6.69 118244.33
## 1167 2015-03-01 X6.69 118246.60
## 1168 2015-04-01 X6.69 118240.44
## 1169 2015-05-01 X6.69 118234.27
## 1170 2015-06-01 X6.69 118337.65
## 1171 2015-07-01 X6.69 118441.03
## 1172 2015-08-01 X6.69 118727.40
## 1173 2015-09-01 X6.69 119013.78
## 1174 2015-10-01 X6.69 119223.28
## 1175 2015-11-01 X6.69 119432.78
## 1176 2015-12-01 X6.69 119370.64
## 1177 2016-01-01 X6.69 119308.51
## 1178 2016-02-01 X6.69 119206.77
## 1179 2016-03-01 X6.69 119105.04
## 1180 2016-04-01 X6.69 118951.86
## 1181 2016-05-01 X6.69 118798.68
## 1182 2016-06-01 X6.69 118603.97
## 1183 2016-07-01 X6.69 118409.26
## 1184 2016-08-01 X6.69 118251.57
## 1185 2016-09-01 X6.69 118093.88
## 1186 2016-10-01 X6.69 118018.68
## 1187 2016-11-01 X6.69 117943.49
## 1188 2016-12-01 X6.69 117929.42
## 1189 2017-01-01 X6.69 117915.35
## 1190 2017-02-01 X6.69 117849.22
## 1191 2017-03-01 X6.69 117783.10
## 1192 2017-04-01 X6.69 117588.19
## 1193 2017-05-01 X6.69 117393.28
## 1194 2017-06-01 X6.69 117160.50
## 1195 2017-07-01 X6.69 116927.72
## 1196 2017-08-01 X6.69 116674.97
## 1197 2017-09-01 X6.69 116422.23
## 1198 2017-10-01 X6.69 116147.27
## 1199 2017-11-01 X6.69 115872.31
## 1200 2017-12-01 X6.69 115577.37
## 1201 1993-01-01 X7.2 137939.62
## 1202 1993-02-01 X7.2 138095.47
## 1203 1993-03-01 X7.2 138251.33
## 1204 1993-04-01 X7.2 138378.21
## 1205 1993-05-01 X7.2 138505.10
## 1206 1993-06-01 X7.2 138622.46
## 1207 1993-07-01 X7.2 138739.81
## 1208 1993-08-01 X7.2 138852.56
## 1209 1993-09-01 X7.2 138965.30
## 1210 1993-10-01 X7.2 138872.59
## 1211 1993-11-01 X7.2 138779.89
## 1212 1993-12-01 X7.2 138519.61
## 1213 1994-01-01 X7.2 138259.33
## 1214 1994-02-01 X7.2 138027.73
## 1215 1994-03-01 X7.2 137796.12
## 1216 1994-04-01 X7.2 137508.17
## 1217 1994-05-01 X7.2 137220.22
## 1218 1994-06-01 X7.2 137003.87
## 1219 1994-07-01 X7.2 136787.53
## 1220 1994-08-01 X7.2 136799.86
## 1221 1994-09-01 X7.2 136812.19
## 1222 1994-10-01 X7.2 137027.98
## 1223 1994-11-01 X7.2 137243.78
## 1224 1994-12-01 X7.2 137396.43
## 1225 1995-01-01 X7.2 137549.09
## 1226 1995-02-01 X7.2 137665.79
## 1227 1995-03-01 X7.2 137782.48
## 1228 1995-04-01 X7.2 137943.66
## 1229 1995-05-01 X7.2 138104.84
## 1230 1995-06-01 X7.2 138446.80
## 1231 1995-07-01 X7.2 138788.75
## 1232 1995-08-01 X7.2 139186.86
## 1233 1995-09-01 X7.2 139584.96
## 1234 1995-10-01 X7.2 139787.99
## 1235 1995-11-01 X7.2 139991.02
## 1236 1995-12-01 X7.2 140049.70
## 1237 1996-01-01 X7.2 140108.39
## 1238 1996-02-01 X7.2 140057.46
## 1239 1996-03-01 X7.2 140006.53
## 1240 1996-04-01 X7.2 139901.88
## 1241 1996-05-01 X7.2 139797.22
## 1242 1996-06-01 X7.2 139597.59
## 1243 1996-07-01 X7.2 139397.96
## 1244 1996-08-01 X7.2 138962.05
## 1245 1996-09-01 X7.2 138526.15
## 1246 1996-10-01 X7.2 138184.24
## 1247 1996-11-01 X7.2 137842.33
## 1248 1996-12-01 X7.2 137721.46
## 1249 1997-01-01 X7.2 137600.60
## 1250 1997-02-01 X7.2 137495.09
## 1251 1997-03-01 X7.2 137389.58
## 1252 1997-04-01 X7.2 137033.35
## 1253 1997-05-01 X7.2 136677.11
## 1254 1997-06-01 X7.2 136437.30
## 1255 1997-07-01 X7.2 136197.48
## 1256 1997-08-01 X7.2 136467.37
## 1257 1997-09-01 X7.2 136737.26
## 1258 1997-10-01 X7.2 136946.98
## 1259 1997-11-01 X7.2 137156.70
## 1260 1997-12-01 X7.2 137069.74
## 1261 1998-01-01 X7.2 136982.77
## 1262 1998-02-01 X7.2 136877.78
## 1263 1998-03-01 X7.2 136772.79
## 1264 1998-04-01 X7.2 136574.33
## 1265 1998-05-01 X7.2 136375.86
## 1266 1998-06-01 X7.2 136308.52
## 1267 1998-07-01 X7.2 136241.18
## 1268 1998-08-01 X7.2 136386.59
## 1269 1998-09-01 X7.2 136532.00
## 1270 1998-10-01 X7.2 136847.02
## 1271 1998-11-01 X7.2 137162.04
## 1272 1998-12-01 X7.2 137473.43
## 1273 1999-01-01 X7.2 137784.82
## 1274 1999-02-01 X7.2 138229.07
## 1275 1999-03-01 X7.2 138673.32
## 1276 1999-04-01 X7.2 139037.61
## 1277 1999-05-01 X7.2 139401.90
## 1278 1999-06-01 X7.2 139319.22
## 1279 1999-07-01 X7.2 139236.54
## 1280 1999-08-01 X7.2 138995.38
## 1281 1999-09-01 X7.2 138754.23
## 1282 1999-10-01 X7.2 138775.66
## 1283 1999-11-01 X7.2 138797.09
## 1284 1999-12-01 X7.2 138783.91
## 1285 2000-01-01 X7.2 138770.74
## 1286 2000-02-01 X7.2 138769.64
## 1287 2000-03-01 X7.2 138768.54
## 1288 2000-04-01 X7.2 138989.45
## 1289 2000-05-01 X7.2 139210.37
## 1290 2000-06-01 X7.2 139609.70
## 1291 2000-07-01 X7.2 140009.03
## 1292 2000-08-01 X7.2 140266.42
## 1293 2000-09-01 X7.2 140523.82
## 1294 2000-10-01 X7.2 140568.48
## 1295 2000-11-01 X7.2 140613.15
## 1296 2000-12-01 X7.2 140546.04
## 1297 2001-01-01 X7.2 140478.94
## 1298 2001-02-01 X7.2 140209.53
## 1299 2001-03-01 X7.2 139940.12
## 1300 2001-04-01 X7.2 139582.66
## 1301 2001-05-01 X7.2 139225.19
## 1302 2001-06-01 X7.2 138956.00
## 1303 2001-07-01 X7.2 138686.82
## 1304 2001-08-01 X7.2 138637.72
## 1305 2001-09-01 X7.2 138588.63
## 1306 2001-10-01 X7.2 138631.80
## 1307 2001-11-01 X7.2 138674.98
## 1308 2001-12-01 X7.2 138714.91
## 1309 2002-01-01 X7.2 138754.84
## 1310 2002-02-01 X7.2 138774.26
## 1311 2002-03-01 X7.2 138793.67
## 1312 2002-04-01 X7.2 138685.55
## 1313 2002-05-01 X7.2 138577.43
## 1314 2002-06-01 X7.2 138185.44
## 1315 2002-07-01 X7.2 137793.45
## 1316 2002-08-01 X7.2 137292.86
## 1317 2002-09-01 X7.2 136792.26
## 1318 2002-10-01 X7.2 136507.09
## 1319 2002-11-01 X7.2 136221.93
## 1320 2002-12-01 X7.2 136162.04
## 1321 2003-01-01 X7.2 136102.16
## 1322 2003-02-01 X7.2 136093.92
## 1323 2003-03-01 X7.2 136085.69
## 1324 2003-04-01 X7.2 136172.62
## 1325 2003-05-01 X7.2 136259.56
## 1326 2003-06-01 X7.2 136327.28
## 1327 2003-07-01 X7.2 136395.01
## 1328 2003-08-01 X7.2 136397.72
## 1329 2003-09-01 X7.2 136400.43
## 1330 2003-10-01 X7.2 136358.83
## 1331 2003-11-01 X7.2 136317.22
## 1332 2003-12-01 X7.2 136344.40
## 1333 2004-01-01 X7.2 136371.59
## 1334 2004-02-01 X7.2 136556.39
## 1335 2004-03-01 X7.2 136741.20
## 1336 2004-04-01 X7.2 137115.03
## 1337 2004-05-01 X7.2 137488.87
## 1338 2004-06-01 X7.2 138082.01
## 1339 2004-07-01 X7.2 138675.16
## 1340 2004-08-01 X7.2 139298.16
## 1341 2004-09-01 X7.2 139921.16
## 1342 2004-10-01 X7.2 140217.52
## 1343 2004-11-01 X7.2 140513.89
## 1344 2004-12-01 X7.2 140550.68
## 1345 2005-01-01 X7.2 140587.47
## 1346 2005-02-01 X7.2 140522.37
## 1347 2005-03-01 X7.2 140457.26
## 1348 2005-04-01 X7.2 140236.33
## 1349 2005-05-01 X7.2 140015.39
## 1350 2005-06-01 X7.2 139448.38
## 1351 2005-07-01 X7.2 138881.36
## 1352 2005-08-01 X7.2 138317.66
## 1353 2005-09-01 X7.2 137753.96
## 1354 2005-10-01 X7.2 137610.52
## 1355 2005-11-01 X7.2 137467.08
## 1356 2005-12-01 X7.2 137417.75
## 1357 2006-01-01 X7.2 137368.42
## 1358 2006-02-01 X7.2 137326.68
## 1359 2006-03-01 X7.2 137284.93
## 1360 2006-04-01 X7.2 137397.00
## 1361 2006-05-01 X7.2 137509.07
## 1362 2006-06-01 X7.2 137510.18
## 1363 2006-07-01 X7.2 137511.28
## 1364 2006-08-01 X7.2 137076.41
## 1365 2006-09-01 X7.2 136641.54
## 1366 2006-10-01 X7.2 136116.96
## 1367 2006-11-01 X7.2 135592.39
## 1368 2006-12-01 X7.2 135354.12
## 1369 2007-01-01 X7.2 135115.86
## 1370 2007-02-01 X7.2 135022.77
## 1371 2007-03-01 X7.2 134929.69
## 1372 2007-04-01 X7.2 134854.60
## 1373 2007-05-01 X7.2 134779.51
## 1374 2007-06-01 X7.2 134894.44
## 1375 2007-07-01 X7.2 135009.37
## 1376 2007-08-01 X7.2 135426.85
## 1377 2007-09-01 X7.2 135844.32
## 1378 2007-10-01 X7.2 136277.29
## 1379 2007-11-01 X7.2 136710.25
## 1380 2007-12-01 X7.2 137000.68
## 1381 2008-01-01 X7.2 137291.11
## 1382 2008-02-01 X7.2 137578.40
## 1383 2008-03-01 X7.2 137865.69
## 1384 2008-04-01 X7.2 138209.90
## 1385 2008-05-01 X7.2 138554.12
## 1386 2008-06-01 X7.2 138731.73
## 1387 2008-07-01 X7.2 138909.33
## 1388 2008-08-01 X7.2 138784.71
## 1389 2008-09-01 X7.2 138660.09
## 1390 2008-10-01 X7.2 138397.53
## 1391 2008-11-01 X7.2 138134.97
## 1392 2008-12-01 X7.2 138004.08
## 1393 2009-01-01 X7.2 137873.20
## 1394 2009-02-01 X7.2 137809.72
## 1395 2009-03-01 X7.2 137746.25
## 1396 2009-04-01 X7.2 137949.81
## 1397 2009-05-01 X7.2 138153.36
## 1398 2009-06-01 X7.2 138548.13
## 1399 2009-07-01 X7.2 138942.89
## 1400 2009-08-01 X7.2 139269.88
## 1401 2009-09-01 X7.2 139596.87
## 1402 2009-10-01 X7.2 139621.03
## 1403 2009-11-01 X7.2 139645.19
## 1404 2009-12-01 X7.2 139521.78
## 1405 2010-01-01 X7.2 139398.36
## 1406 2010-02-01 X7.2 139231.39
## 1407 2010-03-01 X7.2 139064.41
## 1408 2010-04-01 X7.2 138894.26
## 1409 2010-05-01 X7.2 138724.10
## 1410 2010-06-01 X7.2 138440.21
## 1411 2010-07-01 X7.2 138156.33
## 1412 2010-08-01 X7.2 137967.79
## 1413 2010-09-01 X7.2 137779.25
## 1414 2010-10-01 X7.2 137855.30
## 1415 2010-11-01 X7.2 137931.35
## 1416 2010-12-01 X7.2 138151.46
## 1417 2011-01-01 X7.2 138371.56
## 1418 2011-02-01 X7.2 138300.06
## 1419 2011-03-01 X7.2 138228.56
## 1420 2011-04-01 X7.2 138069.06
## 1421 2011-05-01 X7.2 137909.55
## 1422 2011-06-01 X7.2 137787.68
## 1423 2011-07-01 X7.2 137665.82
## 1424 2011-08-01 X7.2 137533.37
## 1425 2011-09-01 X7.2 137400.91
## 1426 2011-10-01 X7.2 137165.31
## 1427 2011-11-01 X7.2 136929.71
## 1428 2011-12-01 X7.2 136893.58
## 1429 2012-01-01 X7.2 136857.45
## 1430 2012-02-01 X7.2 137145.30
## 1431 2012-03-01 X7.2 137433.14
## 1432 2012-04-01 X7.2 137890.87
## 1433 2012-05-01 X7.2 138348.61
## 1434 2012-06-01 X7.2 138864.48
## 1435 2012-07-01 X7.2 139380.36
## 1436 2012-08-01 X7.2 140058.72
## 1437 2012-09-01 X7.2 140737.08
## 1438 2012-10-01 X7.2 141162.26
## 1439 2012-11-01 X7.2 141587.44
## 1440 2012-12-01 X7.2 141627.04
## 1441 2013-01-01 X7.2 141666.63
## 1442 2013-02-01 X7.2 141406.42
## 1443 2013-03-01 X7.2 141146.20
## 1444 2013-04-01 X7.2 140663.02
## 1445 2013-05-01 X7.2 140179.84
## 1446 2013-06-01 X7.2 139805.36
## 1447 2013-07-01 X7.2 139430.88
## 1448 2013-08-01 X7.2 139116.73
## 1449 2013-09-01 X7.2 138802.58
## 1450 2013-10-01 X7.2 138531.02
## 1451 2013-11-01 X7.2 138259.46
## 1452 2013-12-01 X7.2 138220.50
## 1453 2014-01-01 X7.2 138181.55
## 1454 2014-02-01 X7.2 138297.16
## 1455 2014-03-01 X7.2 138412.76
## 1456 2014-04-01 X7.2 138616.75
## 1457 2014-05-01 X7.2 138820.74
## 1458 2014-06-01 X7.2 138901.64
## 1459 2014-07-01 X7.2 138982.53
## 1460 2014-08-01 X7.2 138729.32
## 1461 2014-09-01 X7.2 138476.11
## 1462 2014-10-01 X7.2 138303.58
## 1463 2014-11-01 X7.2 138131.06
## 1464 2014-12-01 X7.2 138078.91
## 1465 2015-01-01 X7.2 138026.77
## 1466 2015-02-01 X7.2 138008.45
## 1467 2015-03-01 X7.2 137990.13
## 1468 2015-04-01 X7.2 138004.97
## 1469 2015-05-01 X7.2 138019.81
## 1470 2015-06-01 X7.2 138148.95
## 1471 2015-07-01 X7.2 138278.09
## 1472 2015-08-01 X7.2 138608.18
## 1473 2015-09-01 X7.2 138938.27
## 1474 2015-10-01 X7.2 139202.87
## 1475 2015-11-01 X7.2 139467.47
## 1476 2015-12-01 X7.2 139424.11
## 1477 2016-01-01 X7.2 139380.75
## 1478 2016-02-01 X7.2 139230.96
## 1479 2016-03-01 X7.2 139081.16
## 1480 2016-04-01 X7.2 138853.60
## 1481 2016-05-01 X7.2 138626.05
## 1482 2016-06-01 X7.2 138389.51
## 1483 2016-07-01 X7.2 138152.97
## 1484 2016-08-01 X7.2 137965.89
## 1485 2016-09-01 X7.2 137778.82
## 1486 2016-10-01 X7.2 137671.63
## 1487 2016-11-01 X7.2 137564.45
## 1488 2016-12-01 X7.2 137565.96
## 1489 2017-01-01 X7.2 137567.47
## 1490 2017-02-01 X7.2 137514.51
## 1491 2017-03-01 X7.2 137461.55
## 1492 2017-04-01 X7.2 137209.78
## 1493 2017-05-01 X7.2 136958.01
## 1494 2017-06-01 X7.2 136678.36
## 1495 2017-07-01 X7.2 136398.71
## 1496 2017-08-01 X7.2 136098.15
## 1497 2017-09-01 X7.2 135797.60
## 1498 2017-10-01 X7.2 135464.30
## 1499 2017-11-01 X7.2 135130.99
## 1500 2017-12-01 X7.2 134768.86
## 1501 1993-01-01 X7.72 164659.23
## 1502 1993-02-01 X7.72 165001.91
## 1503 1993-03-01 X7.72 165344.59
## 1504 1993-04-01 X7.72 165654.33
## 1505 1993-05-01 X7.72 165964.08
## 1506 1993-06-01 X7.72 166263.91
## 1507 1993-07-01 X7.72 166563.74
## 1508 1993-08-01 X7.72 166847.91
## 1509 1993-09-01 X7.72 167132.09
## 1510 1993-10-01 X7.72 167108.45
## 1511 1993-11-01 X7.72 167084.80
## 1512 1993-12-01 X7.72 166813.28
## 1513 1994-01-01 X7.72 166541.76
## 1514 1994-02-01 X7.72 166275.18
## 1515 1994-03-01 X7.72 166008.60
## 1516 1994-04-01 X7.72 165607.63
## 1517 1994-05-01 X7.72 165206.67
## 1518 1994-06-01 X7.72 164853.41
## 1519 1994-07-01 X7.72 164500.16
## 1520 1994-08-01 X7.72 164385.43
## 1521 1994-09-01 X7.72 164270.71
## 1522 1994-10-01 X7.72 164391.30
## 1523 1994-11-01 X7.72 164511.90
## 1524 1994-12-01 X7.72 164621.84
## 1525 1995-01-01 X7.72 164731.78
## 1526 1995-02-01 X7.72 164876.12
## 1527 1995-03-01 X7.72 165020.45
## 1528 1995-04-01 X7.72 165242.50
## 1529 1995-05-01 X7.72 165464.55
## 1530 1995-06-01 X7.72 165818.98
## 1531 1995-07-01 X7.72 166173.42
## 1532 1995-08-01 X7.72 166574.90
## 1533 1995-09-01 X7.72 166976.37
## 1534 1995-10-01 X7.72 167180.97
## 1535 1995-11-01 X7.72 167385.57
## 1536 1995-12-01 X7.72 167415.42
## 1537 1996-01-01 X7.72 167445.27
## 1538 1996-02-01 X7.72 167352.72
## 1539 1996-03-01 X7.72 167260.18
## 1540 1996-04-01 X7.72 167190.56
## 1541 1996-05-01 X7.72 167120.93
## 1542 1996-06-01 X7.72 167045.96
## 1543 1996-07-01 X7.72 166970.99
## 1544 1996-08-01 X7.72 166618.39
## 1545 1996-09-01 X7.72 166265.78
## 1546 1996-10-01 X7.72 165908.96
## 1547 1996-11-01 X7.72 165552.14
## 1548 1996-12-01 X7.72 165402.27
## 1549 1997-01-01 X7.72 165252.40
## 1550 1997-02-01 X7.72 165090.24
## 1551 1997-03-01 X7.72 164928.08
## 1552 1997-04-01 X7.72 164444.90
## 1553 1997-05-01 X7.72 163961.72
## 1554 1997-06-01 X7.72 163608.91
## 1555 1997-07-01 X7.72 163256.11
## 1556 1997-08-01 X7.72 163531.69
## 1557 1997-09-01 X7.72 163807.26
## 1558 1997-10-01 X7.72 164049.53
## 1559 1997-11-01 X7.72 164291.79
## 1560 1997-12-01 X7.72 164224.57
## 1561 1998-01-01 X7.72 164157.35
## 1562 1998-02-01 X7.72 164095.44
## 1563 1998-03-01 X7.72 164033.53
## 1564 1998-04-01 X7.72 163837.94
## 1565 1998-05-01 X7.72 163642.36
## 1566 1998-06-01 X7.72 163499.37
## 1567 1998-07-01 X7.72 163356.39
## 1568 1998-08-01 X7.72 163427.71
## 1569 1998-09-01 X7.72 163499.02
## 1570 1998-10-01 X7.72 163799.31
## 1571 1998-11-01 X7.72 164099.60
## 1572 1998-12-01 X7.72 164460.57
## 1573 1999-01-01 X7.72 164821.54
## 1574 1999-02-01 X7.72 165409.18
## 1575 1999-03-01 X7.72 165996.82
## 1576 1999-04-01 X7.72 166530.87
## 1577 1999-05-01 X7.72 167064.93
## 1578 1999-06-01 X7.72 167097.18
## 1579 1999-07-01 X7.72 167129.43
## 1580 1999-08-01 X7.72 166911.47
## 1581 1999-09-01 X7.72 166693.52
## 1582 1999-10-01 X7.72 166712.75
## 1583 1999-11-01 X7.72 166731.98
## 1584 1999-12-01 X7.72 166658.88
## 1585 2000-01-01 X7.72 166585.77
## 1586 2000-02-01 X7.72 166522.25
## 1587 2000-03-01 X7.72 166458.73
## 1588 2000-04-01 X7.72 166641.49
## 1589 2000-05-01 X7.72 166824.26
## 1590 2000-06-01 X7.72 167171.15
## 1591 2000-07-01 X7.72 167518.04
## 1592 2000-08-01 X7.72 167718.79
## 1593 2000-09-01 X7.72 167919.53
## 1594 2000-10-01 X7.72 167948.70
## 1595 2000-11-01 X7.72 167977.86
## 1596 2000-12-01 X7.72 167926.67
## 1597 2001-01-01 X7.72 167875.48
## 1598 2001-02-01 X7.72 167635.80
## 1599 2001-03-01 X7.72 167396.12
## 1600 2001-04-01 X7.72 167105.48
## 1601 2001-05-01 X7.72 166814.84
## 1602 2001-06-01 X7.72 166676.06
## 1603 2001-07-01 X7.72 166537.29
## 1604 2001-08-01 X7.72 166579.45
## 1605 2001-09-01 X7.72 166621.61
## 1606 2001-10-01 X7.72 166683.41
## 1607 2001-11-01 X7.72 166745.22
## 1608 2001-12-01 X7.72 166770.85
## 1609 2002-01-01 X7.72 166796.49
## 1610 2002-02-01 X7.72 166808.75
## 1611 2002-03-01 X7.72 166821.02
## 1612 2002-04-01 X7.72 166664.68
## 1613 2002-05-01 X7.72 166508.34
## 1614 2002-06-01 X7.72 165993.92
## 1615 2002-07-01 X7.72 165479.51
## 1616 2002-08-01 X7.72 164853.37
## 1617 2002-09-01 X7.72 164227.24
## 1618 2002-10-01 X7.72 163901.86
## 1619 2002-11-01 X7.72 163576.49
## 1620 2002-12-01 X7.72 163507.85
## 1621 2003-01-01 X7.72 163439.21
## 1622 2003-02-01 X7.72 163446.27
## 1623 2003-03-01 X7.72 163453.33
## 1624 2003-04-01 X7.72 163590.89
## 1625 2003-05-01 X7.72 163728.46
## 1626 2003-06-01 X7.72 163811.28
## 1627 2003-07-01 X7.72 163894.11
## 1628 2003-08-01 X7.72 163873.35
## 1629 2003-09-01 X7.72 163852.58
## 1630 2003-10-01 X7.72 163809.04
## 1631 2003-11-01 X7.72 163765.49
## 1632 2003-12-01 X7.72 163803.21
## 1633 2004-01-01 X7.72 163840.92
## 1634 2004-02-01 X7.72 164046.84
## 1635 2004-03-01 X7.72 164252.75
## 1636 2004-04-01 X7.72 164703.18
## 1637 2004-05-01 X7.72 165153.62
## 1638 2004-06-01 X7.72 165898.53
## 1639 2004-07-01 X7.72 166643.44
## 1640 2004-08-01 X7.72 167407.39
## 1641 2004-09-01 X7.72 168171.33
## 1642 2004-10-01 X7.72 168499.92
## 1643 2004-11-01 X7.72 168828.50
## 1644 2004-12-01 X7.72 168849.96
## 1645 2005-01-01 X7.72 168871.42
## 1646 2005-02-01 X7.72 168775.09
## 1647 2005-03-01 X7.72 168678.76
## 1648 2005-04-01 X7.72 168379.05
## 1649 2005-05-01 X7.72 168079.34
## 1650 2005-06-01 X7.72 167390.13
## 1651 2005-07-01 X7.72 166700.92
## 1652 2005-08-01 X7.72 166031.04
## 1653 2005-09-01 X7.72 165361.17
## 1654 2005-10-01 X7.72 165187.17
## 1655 2005-11-01 X7.72 165013.18
## 1656 2005-12-01 X7.72 164954.76
## 1657 2006-01-01 X7.72 164896.35
## 1658 2006-02-01 X7.72 164881.44
## 1659 2006-03-01 X7.72 164866.52
## 1660 2006-04-01 X7.72 165078.29
## 1661 2006-05-01 X7.72 165290.07
## 1662 2006-06-01 X7.72 165343.73
## 1663 2006-07-01 X7.72 165397.40
## 1664 2006-08-01 X7.72 164892.78
## 1665 2006-09-01 X7.72 164388.16
## 1666 2006-10-01 X7.72 163771.63
## 1667 2006-11-01 X7.72 163155.11
## 1668 2006-12-01 X7.72 162845.67
## 1669 2007-01-01 X7.72 162536.23
## 1670 2007-02-01 X7.72 162365.28
## 1671 2007-03-01 X7.72 162194.33
## 1672 2007-04-01 X7.72 162043.19
## 1673 2007-05-01 X7.72 161892.05
## 1674 2007-06-01 X7.72 162015.06
## 1675 2007-07-01 X7.72 162138.06
## 1676 2007-08-01 X7.72 162718.47
## 1677 2007-09-01 X7.72 163298.88
## 1678 2007-10-01 X7.72 163906.59
## 1679 2007-11-01 X7.72 164514.30
## 1680 2007-12-01 X7.72 164922.03
## 1681 2008-01-01 X7.72 165329.75
## 1682 2008-02-01 X7.72 165692.53
## 1683 2008-03-01 X7.72 166055.31
## 1684 2008-04-01 X7.72 166409.46
## 1685 2008-05-01 X7.72 166763.61
## 1686 2008-06-01 X7.72 166924.50
## 1687 2008-07-01 X7.72 167085.39
## 1688 2008-08-01 X7.72 166890.91
## 1689 2008-09-01 X7.72 166696.42
## 1690 2008-10-01 X7.72 166313.36
## 1691 2008-11-01 X7.72 165930.30
## 1692 2008-12-01 X7.72 165752.68
## 1693 2009-01-01 X7.72 165575.05
## 1694 2009-02-01 X7.72 165512.06
## 1695 2009-03-01 X7.72 165449.06
## 1696 2009-04-01 X7.72 165771.02
## 1697 2009-05-01 X7.72 166092.98
## 1698 2009-06-01 X7.72 166629.41
## 1699 2009-07-01 X7.72 167165.85
## 1700 2009-08-01 X7.72 167582.61
## 1701 2009-09-01 X7.72 167999.37
## 1702 2009-10-01 X7.72 168104.85
## 1703 2009-11-01 X7.72 168210.33
## 1704 2009-12-01 X7.72 168116.94
## 1705 2010-01-01 X7.72 168023.54
## 1706 2010-02-01 X7.72 167845.78
## 1707 2010-03-01 X7.72 167668.01
## 1708 2010-04-01 X7.72 167455.75
## 1709 2010-05-01 X7.72 167243.48
## 1710 2010-06-01 X7.72 166874.75
## 1711 2010-07-01 X7.72 166506.01
## 1712 2010-08-01 X7.72 166254.34
## 1713 2010-09-01 X7.72 166002.67
## 1714 2010-10-01 X7.72 166034.03
## 1715 2010-11-01 X7.72 166065.39
## 1716 2010-12-01 X7.72 166262.48
## 1717 2011-01-01 X7.72 166459.56
## 1718 2011-02-01 X7.72 166295.08
## 1719 2011-03-01 X7.72 166130.59
## 1720 2011-04-01 X7.72 165862.99
## 1721 2011-05-01 X7.72 165595.40
## 1722 2011-06-01 X7.72 165390.70
## 1723 2011-07-01 X7.72 165186.00
## 1724 2011-08-01 X7.72 164986.71
## 1725 2011-09-01 X7.72 164787.42
## 1726 2011-10-01 X7.72 164491.89
## 1727 2011-11-01 X7.72 164196.36
## 1728 2011-12-01 X7.72 164164.86
## 1729 2012-01-01 X7.72 164133.37
## 1730 2012-02-01 X7.72 164486.75
## 1731 2012-03-01 X7.72 164840.14
## 1732 2012-04-01 X7.72 165398.98
## 1733 2012-05-01 X7.72 165957.82
## 1734 2012-06-01 X7.72 166620.98
## 1735 2012-07-01 X7.72 167284.14
## 1736 2012-08-01 X7.72 168176.58
## 1737 2012-09-01 X7.72 169069.01
## 1738 2012-10-01 X7.72 169612.20
## 1739 2012-11-01 X7.72 170155.40
## 1740 2012-12-01 X7.72 170206.94
## 1741 2013-01-01 X7.72 170258.48
## 1742 2013-02-01 X7.72 169949.90
## 1743 2013-03-01 X7.72 169641.33
## 1744 2013-04-01 X7.72 169095.05
## 1745 2013-05-01 X7.72 168548.77
## 1746 2013-06-01 X7.72 168245.98
## 1747 2013-07-01 X7.72 167943.20
## 1748 2013-08-01 X7.72 167747.36
## 1749 2013-09-01 X7.72 167551.52
## 1750 2013-10-01 X7.72 167324.12
## 1751 2013-11-01 X7.72 167096.71
## 1752 2013-12-01 X7.72 167072.93
## 1753 2014-01-01 X7.72 167049.15
## 1754 2014-02-01 X7.72 167166.80
## 1755 2014-03-01 X7.72 167284.44
## 1756 2014-04-01 X7.72 167427.75
## 1757 2014-05-01 X7.72 167571.06
## 1758 2014-06-01 X7.72 167465.55
## 1759 2014-07-01 X7.72 167360.04
## 1760 2014-08-01 X7.72 166921.78
## 1761 2014-09-01 X7.72 166483.52
## 1762 2014-10-01 X7.72 166294.83
## 1763 2014-11-01 X7.72 166106.15
## 1764 2014-12-01 X7.72 166054.20
## 1765 2015-01-01 X7.72 166002.25
## 1766 2015-02-01 X7.72 165955.75
## 1767 2015-03-01 X7.72 165909.26
## 1768 2015-04-01 X7.72 165920.29
## 1769 2015-05-01 X7.72 165931.33
## 1770 2015-06-01 X7.72 166128.17
## 1771 2015-07-01 X7.72 166325.01
## 1772 2015-08-01 X7.72 166802.79
## 1773 2015-09-01 X7.72 167280.57
## 1774 2015-10-01 X7.72 167679.07
## 1775 2015-11-01 X7.72 168077.56
## 1776 2015-12-01 X7.72 168089.21
## 1777 2016-01-01 X7.72 168100.85
## 1778 2016-02-01 X7.72 167918.56
## 1779 2016-03-01 X7.72 167736.26
## 1780 2016-04-01 X7.72 167365.12
## 1781 2016-05-01 X7.72 166993.97
## 1782 2016-06-01 X7.72 166564.50
## 1783 2016-07-01 X7.72 166135.02
## 1784 2016-08-01 X7.72 165791.47
## 1785 2016-09-01 X7.72 165447.93
## 1786 2016-10-01 X7.72 165268.49
## 1787 2016-11-01 X7.72 165089.06
## 1788 2016-12-01 X7.72 165097.07
## 1789 2017-01-01 X7.72 165105.07
## 1790 2017-02-01 X7.72 165065.69
## 1791 2017-03-01 X7.72 165026.32
## 1792 2017-04-01 X7.72 164734.75
## 1793 2017-05-01 X7.72 164443.18
## 1794 2017-06-01 X7.72 164123.19
## 1795 2017-07-01 X7.72 163803.19
## 1796 2017-08-01 X7.72 163449.32
## 1797 2017-09-01 X7.72 163095.46
## 1798 2017-10-01 X7.72 162692.67
## 1799 2017-11-01 X7.72 162289.89
## 1800 2017-12-01 X7.72 161847.87
## 1801 1993-01-01 X8.23 203655.93
## 1802 1993-02-01 X8.23 204133.64
## 1803 1993-03-01 X8.23 204611.35
## 1804 1993-04-01 X8.23 205044.50
## 1805 1993-05-01 X8.23 205477.65
## 1806 1993-06-01 X8.23 205896.29
## 1807 1993-07-01 X8.23 206314.93
## 1808 1993-08-01 X8.23 206731.50
## 1809 1993-09-01 X8.23 207148.07
## 1810 1993-10-01 X8.23 207232.51
## 1811 1993-11-01 X8.23 207316.96
## 1812 1993-12-01 X8.23 207040.05
## 1813 1994-01-01 X8.23 206763.14
## 1814 1994-02-01 X8.23 206421.38
## 1815 1994-03-01 X8.23 206079.61
## 1816 1994-04-01 X8.23 205543.89
## 1817 1994-05-01 X8.23 205008.17
## 1818 1994-06-01 X8.23 204646.27
## 1819 1994-07-01 X8.23 204284.37
## 1820 1994-08-01 X8.23 204284.29
## 1821 1994-09-01 X8.23 204284.22
## 1822 1994-10-01 X8.23 204472.21
## 1823 1994-11-01 X8.23 204660.21
## 1824 1994-12-01 X8.23 204810.95
## 1825 1995-01-01 X8.23 204961.69
## 1826 1995-02-01 X8.23 205148.22
## 1827 1995-03-01 X8.23 205334.75
## 1828 1995-04-01 X8.23 205544.87
## 1829 1995-05-01 X8.23 205754.98
## 1830 1995-06-01 X8.23 205934.96
## 1831 1995-07-01 X8.23 206114.94
## 1832 1995-08-01 X8.23 206298.36
## 1833 1995-09-01 X8.23 206481.79
## 1834 1995-10-01 X8.23 206572.02
## 1835 1995-11-01 X8.23 206662.24
## 1836 1995-12-01 X8.23 206663.62
## 1837 1996-01-01 X8.23 206665.00
## 1838 1996-02-01 X8.23 206547.16
## 1839 1996-03-01 X8.23 206429.32
## 1840 1996-04-01 X8.23 206506.11
## 1841 1996-05-01 X8.23 206582.91
## 1842 1996-06-01 X8.23 206846.94
## 1843 1996-07-01 X8.23 207110.96
## 1844 1996-08-01 X8.23 206928.38
## 1845 1996-09-01 X8.23 206745.80
## 1846 1996-10-01 X8.23 206337.21
## 1847 1996-11-01 X8.23 205928.62
## 1848 1996-12-01 X8.23 205714.98
## 1849 1997-01-01 X8.23 205501.35
## 1850 1997-02-01 X8.23 205220.40
## 1851 1997-03-01 X8.23 204939.45
## 1852 1997-04-01 X8.23 204148.29
## 1853 1997-05-01 X8.23 203357.12
## 1854 1997-06-01 X8.23 202745.29
## 1855 1997-07-01 X8.23 202133.45
## 1856 1997-08-01 X8.23 202486.57
## 1857 1997-09-01 X8.23 202839.69
## 1858 1997-10-01 X8.23 203190.44
## 1859 1997-11-01 X8.23 203541.19
## 1860 1997-12-01 X8.23 203468.04
## 1861 1998-01-01 X8.23 203394.89
## 1862 1998-02-01 X8.23 203342.08
## 1863 1998-03-01 X8.23 203289.28
## 1864 1998-04-01 X8.23 203044.18
## 1865 1998-05-01 X8.23 202799.07
## 1866 1998-06-01 X8.23 202589.60
## 1867 1998-07-01 X8.23 202380.12
## 1868 1998-08-01 X8.23 202411.95
## 1869 1998-09-01 X8.23 202443.77
## 1870 1998-10-01 X8.23 202759.46
## 1871 1998-11-01 X8.23 203075.16
## 1872 1998-12-01 X8.23 203501.93
## 1873 1999-01-01 X8.23 203928.71
## 1874 1999-02-01 X8.23 204675.14
## 1875 1999-03-01 X8.23 205421.57
## 1876 1999-04-01 X8.23 206081.77
## 1877 1999-05-01 X8.23 206741.96
## 1878 1999-06-01 X8.23 206785.97
## 1879 1999-07-01 X8.23 206829.98
## 1880 1999-08-01 X8.23 206606.69
## 1881 1999-09-01 X8.23 206383.40
## 1882 1999-10-01 X8.23 206477.89
## 1883 1999-11-01 X8.23 206572.38
## 1884 1999-12-01 X8.23 206479.58
## 1885 2000-01-01 X8.23 206386.79
## 1886 2000-02-01 X8.23 206260.90
## 1887 2000-03-01 X8.23 206135.01
## 1888 2000-04-01 X8.23 206436.71
## 1889 2000-05-01 X8.23 206738.42
## 1890 2000-06-01 X8.23 207325.35
## 1891 2000-07-01 X8.23 207912.27
## 1892 2000-08-01 X8.23 208236.93
## 1893 2000-09-01 X8.23 208561.59
## 1894 2000-10-01 X8.23 208613.08
## 1895 2000-11-01 X8.23 208664.56
## 1896 2000-12-01 X8.23 208615.29
## 1897 2001-01-01 X8.23 208566.01
## 1898 2001-02-01 X8.23 208237.15
## 1899 2001-03-01 X8.23 207908.28
## 1900 2001-04-01 X8.23 207419.98
## 1901 2001-05-01 X8.23 206931.69
## 1902 2001-06-01 X8.23 206586.04
## 1903 2001-07-01 X8.23 206240.39
## 1904 2001-08-01 X8.23 206214.03
## 1905 2001-09-01 X8.23 206187.68
## 1906 2001-10-01 X8.23 206244.79
## 1907 2001-11-01 X8.23 206301.90
## 1908 2001-12-01 X8.23 206341.17
## 1909 2002-01-01 X8.23 206380.44
## 1910 2002-02-01 X8.23 206394.35
## 1911 2002-03-01 X8.23 206408.27
## 1912 2002-04-01 X8.23 206188.29
## 1913 2002-05-01 X8.23 205968.31
## 1914 2002-06-01 X8.23 205296.73
## 1915 2002-07-01 X8.23 204625.14
## 1916 2002-08-01 X8.23 203806.16
## 1917 2002-09-01 X8.23 202987.19
## 1918 2002-10-01 X8.23 202538.52
## 1919 2002-11-01 X8.23 202089.86
## 1920 2002-12-01 X8.23 202002.54
## 1921 2003-01-01 X8.23 201915.22
## 1922 2003-02-01 X8.23 201970.75
## 1923 2003-03-01 X8.23 202026.28
## 1924 2003-04-01 X8.23 202313.59
## 1925 2003-05-01 X8.23 202600.89
## 1926 2003-06-01 X8.23 202820.74
## 1927 2003-07-01 X8.23 203040.60
## 1928 2003-08-01 X8.23 203051.17
## 1929 2003-09-01 X8.23 203061.74
## 1930 2003-10-01 X8.23 202970.05
## 1931 2003-11-01 X8.23 202878.36
## 1932 2003-12-01 X8.23 202882.51
## 1933 2004-01-01 X8.23 202886.65
## 1934 2004-02-01 X8.23 203086.23
## 1935 2004-03-01 X8.23 203285.80
## 1936 2004-04-01 X8.23 203733.31
## 1937 2004-05-01 X8.23 204180.82
## 1938 2004-06-01 X8.23 205061.21
## 1939 2004-07-01 X8.23 205941.61
## 1940 2004-08-01 X8.23 206947.80
## 1941 2004-09-01 X8.23 207953.99
## 1942 2004-10-01 X8.23 208420.13
## 1943 2004-11-01 X8.23 208886.27
## 1944 2004-12-01 X8.23 208920.84
## 1945 2005-01-01 X8.23 208955.40
## 1946 2005-02-01 X8.23 208894.31
## 1947 2005-03-01 X8.23 208833.21
## 1948 2005-04-01 X8.23 208581.22
## 1949 2005-05-01 X8.23 208329.22
## 1950 2005-06-01 X8.23 207491.79
## 1951 2005-07-01 X8.23 206654.36
## 1952 2005-08-01 X8.23 205752.29
## 1953 2005-09-01 X8.23 204850.22
## 1954 2005-10-01 X8.23 204585.16
## 1955 2005-11-01 X8.23 204320.10
## 1956 2005-12-01 X8.23 204198.13
## 1957 2006-01-01 X8.23 204076.15
## 1958 2006-02-01 X8.23 203987.72
## 1959 2006-03-01 X8.23 203899.30
## 1960 2006-04-01 X8.23 204076.77
## 1961 2006-05-01 X8.23 204254.24
## 1962 2006-06-01 X8.23 204288.65
## 1963 2006-07-01 X8.23 204323.05
## 1964 2006-08-01 X8.23 203677.35
## 1965 2006-09-01 X8.23 203031.65
## 1966 2006-10-01 X8.23 202258.24
## 1967 2006-11-01 X8.23 201484.84
## 1968 2006-12-01 X8.23 201106.04
## 1969 2007-01-01 X8.23 200727.24
## 1970 2007-02-01 X8.23 200531.73
## 1971 2007-03-01 X8.23 200336.21
## 1972 2007-04-01 X8.23 200219.60
## 1973 2007-05-01 X8.23 200103.00
## 1974 2007-06-01 X8.23 200405.13
## 1975 2007-07-01 X8.23 200707.27
## 1976 2007-08-01 X8.23 201546.67
## 1977 2007-09-01 X8.23 202386.08
## 1978 2007-10-01 X8.23 203186.03
## 1979 2007-11-01 X8.23 203985.99
## 1980 2007-12-01 X8.23 204518.12
## 1981 2008-01-01 X8.23 205050.25
## 1982 2008-02-01 X8.23 205548.88
## 1983 2008-03-01 X8.23 206047.51
## 1984 2008-04-01 X8.23 206480.18
## 1985 2008-05-01 X8.23 206912.86
## 1986 2008-06-01 X8.23 207017.90
## 1987 2008-07-01 X8.23 207122.95
## 1988 2008-08-01 X8.23 206832.07
## 1989 2008-09-01 X8.23 206541.19
## 1990 2008-10-01 X8.23 206064.98
## 1991 2008-11-01 X8.23 205588.77
## 1992 2008-12-01 X8.23 205361.82
## 1993 2009-01-01 X8.23 205134.87
## 1994 2009-02-01 X8.23 205021.88
## 1995 2009-03-01 X8.23 204908.89
## 1996 2009-04-01 X8.23 205260.77
## 1997 2009-05-01 X8.23 205612.65
## 1998 2009-06-01 X8.23 206256.99
## 1999 2009-07-01 X8.23 206901.32
## 2000 2009-08-01 X8.23 207378.32
## 2001 2009-09-01 X8.23 207855.32
## 2002 2009-10-01 X8.23 207936.76
## 2003 2009-11-01 X8.23 208018.19
## 2004 2009-12-01 X8.23 207887.57
## 2005 2010-01-01 X8.23 207756.95
## 2006 2010-02-01 X8.23 207577.38
## 2007 2010-03-01 X8.23 207397.80
## 2008 2010-04-01 X8.23 207147.21
## 2009 2010-05-01 X8.23 206896.61
## 2010 2010-06-01 X8.23 206398.48
## 2011 2010-07-01 X8.23 205900.35
## 2012 2010-08-01 X8.23 205531.12
## 2013 2010-09-01 X8.23 205161.88
## 2014 2010-10-01 X8.23 205134.50
## 2015 2010-11-01 X8.23 205107.11
## 2016 2010-12-01 X8.23 205290.02
## 2017 2011-01-01 X8.23 205472.93
## 2018 2011-02-01 X8.23 205278.20
## 2019 2011-03-01 X8.23 205083.47
## 2020 2011-04-01 X8.23 204849.14
## 2021 2011-05-01 X8.23 204614.80
## 2022 2011-06-01 X8.23 204450.37
## 2023 2011-07-01 X8.23 204285.94
## 2024 2011-08-01 X8.23 204085.49
## 2025 2011-09-01 X8.23 203885.04
## 2026 2011-10-01 X8.23 203584.83
## 2027 2011-11-01 X8.23 203284.61
## 2028 2011-12-01 X8.23 203286.34
## 2029 2012-01-01 X8.23 203288.08
## 2030 2012-02-01 X8.23 203721.44
## 2031 2012-03-01 X8.23 204154.80
## 2032 2012-04-01 X8.23 204880.87
## 2033 2012-05-01 X8.23 205606.93
## 2034 2012-06-01 X8.23 206551.54
## 2035 2012-07-01 X8.23 207496.15
## 2036 2012-08-01 X8.23 208812.84
## 2037 2012-09-01 X8.23 210129.52
## 2038 2012-10-01 X8.23 210969.18
## 2039 2012-11-01 X8.23 211808.84
## 2040 2012-12-01 X8.23 211933.60
## 2041 2013-01-01 X8.23 212058.36
## 2042 2013-02-01 X8.23 211632.22
## 2043 2013-03-01 X8.23 211206.08
## 2044 2013-04-01 X8.23 210365.68
## 2045 2013-05-01 X8.23 209525.27
## 2046 2013-06-01 X8.23 208889.49
## 2047 2013-07-01 X8.23 208253.70
## 2048 2013-08-01 X8.23 207802.07
## 2049 2013-09-01 X8.23 207350.44
## 2050 2013-10-01 X8.23 206965.77
## 2051 2013-11-01 X8.23 206581.10
## 2052 2013-12-01 X8.23 206552.68
## 2053 2014-01-01 X8.23 206524.27
## 2054 2014-02-01 X8.23 206737.59
## 2055 2014-03-01 X8.23 206950.91
## 2056 2014-04-01 X8.23 207244.21
## 2057 2014-05-01 X8.23 207537.52
## 2058 2014-06-01 X8.23 207483.98
## 2059 2014-07-01 X8.23 207430.44
## 2060 2014-08-01 X8.23 206913.25
## 2061 2014-09-01 X8.23 206396.06
## 2062 2014-10-01 X8.23 206226.97
## 2063 2014-11-01 X8.23 206057.88
## 2064 2014-12-01 X8.23 206061.46
## 2065 2015-01-01 X8.23 206065.04
## 2066 2015-02-01 X8.23 206003.64
## 2067 2015-03-01 X8.23 205942.23
## 2068 2015-04-01 X8.23 205883.52
## 2069 2015-05-01 X8.23 205824.81
## 2070 2015-06-01 X8.23 206025.20
## 2071 2015-07-01 X8.23 206225.60
## 2072 2015-08-01 X8.23 206813.41
## 2073 2015-09-01 X8.23 207401.23
## 2074 2015-10-01 X8.23 207890.71
## 2075 2015-11-01 X8.23 208380.20
## 2076 2015-12-01 X8.23 208395.43
## 2077 2016-01-01 X8.23 208410.65
## 2078 2016-02-01 X8.23 208241.17
## 2079 2016-03-01 X8.23 208071.68
## 2080 2016-04-01 X8.23 207658.05
## 2081 2016-05-01 X8.23 207244.43
## 2082 2016-06-01 X8.23 206624.88
## 2083 2016-07-01 X8.23 206005.34
## 2084 2016-08-01 X8.23 205447.29
## 2085 2016-09-01 X8.23 204889.24
## 2086 2016-10-01 X8.23 204621.87
## 2087 2016-11-01 X8.23 204354.50
## 2088 2016-12-01 X8.23 204348.65
## 2089 2017-01-01 X8.23 204342.79
## 2090 2017-02-01 X8.23 204297.50
## 2091 2017-03-01 X8.23 204252.20
## 2092 2017-04-01 X8.23 203888.00
## 2093 2017-05-01 X8.23 203523.80
## 2094 2017-06-01 X8.23 203087.82
## 2095 2017-07-01 X8.23 202651.85
## 2096 2017-08-01 X8.23 202170.96
## 2097 2017-09-01 X8.23 201690.07
## 2098 2017-10-01 X8.23 201142.60
## 2099 2017-11-01 X8.23 200595.13
## 2100 2017-12-01 X8.23 199987.57
Pasamos a dibujar las tendencias, primero en un único gráfico:
ggplot(fuel_speed_df_long, aes(x = date, y = value)) +
geom_line(aes(color = variable), size = 1) +
scale_colour_discrete() +
labs(title = "Fuel consumption trends vs speed (I)", color="speeds") +
ylab("fuel consumption, kg. (fuel oil) or m^3 (gas)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
A continuación las tendencias por separado:
ggplot(fuel_speed_df_long, aes(x = date, y = value)) +
geom_line(aes(color = variable), size = 1) +
facet_wrap(~variable, scales="free", ncol=2) +
scale_colour_discrete() +
labs(title = "Fuel consumption trends vs speed (II)", color="speeds") +
ylab("fuel consumption, kg. (fuel oil) or m^3 (gas)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Se puede observar por las graficas de tendencia de consumo de fuel para las distintas velocidades, tanto la conjunta como por separado, que dichas tendencias guardan si no el mismo muy similar patrón a lo largo del tiempo, diferenciandose solo en los valores de combustible, que como era de esperar son mayores según aumenta la velocidad.
which.max(fuel_speed_trends_df$X5.14)
## [1] 241
which.max(fuel_speed_trends_df$X6.69)
## [1] 241
which.max(fuel_speed_trends_df$X8.23)
## [1] 241
fuel_speed_trends_df$date[which.max(fuel_speed_trends_df$X8.23)]
## [1] "2013-01-01"
Comprobamos que el valor máximo exacto para todas las velocidades se corresponde con Enero de 2013
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.6.2
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
period_high_fuel_consumpt <- fuel_speed_df_long %>%
filter(year(date)>=1999 & year(date)<=2002)
ggplot(period_high_fuel_consumpt, aes(x = date, y = value)) +
geom_line(aes(color = variable), size = 1) +
facet_wrap(~variable, scales="free", ncol=2) +
scale_colour_discrete() +
labs(title = "Period of high fuel consumption: 1999 - 2003", color="speeds") +
ylab("fuel consumption, kg. (fuel oil) or m^3 (gas)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
library(lubridate)
period_high_fluct_fuel_consumpt <- fuel_speed_df_long %>%
filter(year(date)>=2004 & year(date)<=2007)
ggplot(period_high_fluct_fuel_consumpt, aes(x = date, y = value)) +
geom_line(aes(color = variable), size = 1) +
facet_wrap(~variable, scales="free", ncol=2) +
scale_colour_discrete() +
labs(title = "Period of greatest fluctuation: 2005 - 2008", color="speeds") +
ylab("fuel consumption, kg. (fuel oil) or m^3 (gas)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Ya se ha visto anteriormente la casi igualdad en las tendencias de consumo para las distintas velocidades. Pasamos a examinar las series temporales para 3 de las velocidades (mínima,intermedia,máxima):
descrp_fuel_ts <- ts(data = data.frame(speed_5.14=fuel_cons_speed_ts$`5.14`,speed_6.69=fuel_cons_speed_ts$`6.69`,speed_8.23=fuel_cons_speed_ts$`8.23`), start = 1993, frequency = 12)
ts_info(descrp_fuel_ts)
## The descrp_fuel_ts series is a mts object with 3 variables and 300 observations
## Frequency: 12
## Start time: 1993 1
## End time: 2017 12
ts_plot(descrp_fuel_ts,
title = "Fuel consumption (speed)",
type = "multiple")
Como observamos por las gráficas las series temporales para las distintas velocidades tienen un trazado prácticamente idéntico, diferenciandose claro esta en los valores de consumo de fuel (a mayor velocidad más consumo)
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(3,1))
spec_5.14 <- spec.pgram(fuel_cons_speed_ts$`5.14`,taper=0,log='no')
spec_6.69 <- spec.pgram(fuel_cons_speed_ts$`6.69`,taper=0,log='no')
spec_8.23 <- spec.pgram(fuel_cons_speed_ts$`8.23`,taper=0,log='no')
Resulta curioso observar que aparte del periodo anual predominante (al menos en 2) nos aparece otro periodo en distinto grado correspondiente a 2 años.
Completamos este apartado en cuanto a la estacionalidad y existencia de ciclos con las siguientes gráficas. En primer lugar un mapa de calor, en la que dada la similitud observada en las distintas series temporales de consumo por velocidad nos quedaremos para esta gráfica con dos velocidades, la mínima y la máxima.
ts_heatmap(fuel_cons_speed_ts$`5.14`, color = "Reds", title = "Fuel consumption (speed=5.14)")
ts_heatmap(fuel_cons_speed_ts$`8.23`, color = "Reds", title = "Fuel consumption (speed=8.23)")
Podemos observar señales de existencia de ciclo anual, con variaciones debidas quizás a las excepciones lógicas dado el rango de años u otros posibles factores puntuales influyentes (meteorológicos, etc.).
El consumo menor se corresponde con el mes de Julio, con valores bajos también en su anterior y posterior. Los valores más altos se concentran en general en los meses de Noviembre y Diciembre, con más excepciones.
ts_seasonal(fuel_cons_speed_ts$`6.69`,type ="box")
u10_values_pts <- as.data.frame(u10.wind_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(u10_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot U-component wind 10 meters (points)",fill = "points") +
xlab("points") +
ylab("value (m/s)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Veamos a que fechas se corresponden los outliers. Probamos con el punto ‘1’:
box.u10 <- boxplot.stats(u10.wind_pts_ts$`1`)
out_values <- box.u10$out
out_index <- which(u10.wind_pts_ts$`1` %in% box.u10$out)
# get outliers year and month vectors, join and cast to Date
y <- as.character(floor(time(u10.wind_pts_ts$`1`)[out_index]))
m <- as.character(cycle(u10.wind_pts_ts$`1`)[out_index])
out_dates <- as.Date(paste(y,m,"01",sep = "-"))
data.frame(out_dates,out_values)
## out_dates out_values
## 1 2003-01-01 4.951956
## 2 2007-10-01 -5.404785
## 3 2010-12-01 5.106748
Realizada la prueba escribimos funcion para devolver los resultados en todos los puntos:
outliers_date_ts <- function(mesoc_var_pts_ts_list) {
outliers_df <- function(tseries) {
df <- data.frame()
box <- boxplot.stats(tseries)
out_values <- box$out
out_index <- which(tseries %in% box$out)
if (length(out_index) != 0) {
# get outliers year and month vectors, join and cast to Date
y <- as.character(floor(time(tseries)[out_index]))
m <- as.character(cycle(tseries)[out_index])
out_dates <- as.Date(paste(y,m,"01",sep = "-"))
df <- data.frame(out_dates,out_values)
}
return(df)
}
return(lapply(mesoc_var_pts_ts_list,FUN = outliers_df))
}
outliers_date_ts(u10.wind_pts_ts)
## $`1`
## out_dates out_values
## 1 2003-01-01 4.951956
## 2 2007-10-01 -5.404785
## 3 2010-12-01 5.106748
##
## $`2`
## out_dates out_values
## 1 2003-10-01 -4.004072
## 2 2004-01-01 10.585528
## 3 2006-03-01 8.956615
## 4 2007-02-01 9.014463
## 5 2010-02-01 9.559429
##
## $`3`
## out_dates out_values
## 1 1994-02-01 8.626285
## 2 2000-12-01 10.728352
## 3 2001-03-01 9.399051
## 4 2002-01-01 9.038799
## 5 2007-11-01 -2.881028
## 6 2008-11-01 -6.071032
## 7 2009-01-01 8.269623
## 8 2012-09-01 -3.126781
##
## $`4`
## out_dates out_values
## 1 2009-11-01 10.20413
## 2 2014-02-01 11.00004
NOTA: contrastar las fechas con periodos de valores altos y de máxima fluctuación
descrp_u10_ts <- ts(data = data.frame(pnt_1=u10.wind_pts_ts$`1`,pnt_2=u10.wind_pts_ts$`2`,pnt_3=u10.wind_pts_ts$`3`,pnt_4=u10.wind_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_u10_ts, title = "U10-component wind (points)", type = "multiple")
Como era de esperar al ser distintos puntos geográficos los trazados de las series temporales no guardan similitud.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_u10.pt1 <- spec.pgram(u10.wind_pts_ts$`1`,taper=0,log='no')
spec_u10.pt2 <- spec.pgram(u10.wind_pts_ts$`2`,taper=0,log='no')
spec_u10.pt3 <- spec.pgram(u10.wind_pts_ts$`3`,taper=0,log='no')
spec_u10.pt4 <- spec.pgram(u10.wind_pts_ts$`4`,taper=0,log='no')
En los primeros dos puntos se observa la existencia de ciclo al año y 2 años. En las otras 2 se acompañan de otras componentes menores, en mayor grado en el punto 4.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(u10.wind_pts_ts$`1`, color = "Reds", title = "U10-component wind (pt. 1)")
ts_heatmap(u10.wind_pts_ts$`3`, color = "Reds", title = "U10-component wind (pt. 3)")
Sin haber patrón que pueda señalar la existencia de una componente mayoritaria anual, si podemos afirmar que en general los valores máximos se sulen encontrar en Enero y Febrero.
En cualquier caso y como se podía esperar para las variables mesoceánicas éstas presentan una mayor variación y espectogramas más complejos que no presentan una única componente (frecuencia).
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_u10_trends <- ts(data = data.frame(pnt_1=u10.wind_pts_trend$`1`,pnt_2=u10.wind_pts_trend$`2`,pnt_3=u10.wind_pts_trend$`3`,pnt_4=u10.wind_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_u10_trends, title = "U10-component wind and fuel consumption trends (points)", type = "multiple")
Las tendencias entre los puntos presentan distintos trazados como era esperable.
NOTA: COMPARAR CON LOS PERIODOS DE VALORES ALTOS Y DE MÁXIMA FLUCTUACIÓN, POR SI HAY ALGUNA RELACIÓN Primero de valores muy bajos (y distinta distribución como vimos al resto puntos) dificil comparación Resto (¿alta mar?) se observan algunos patrones.
Dado que como hemos visto la tendencia del consumo de fuel para las distintas velocidades siguen un patrón muy similar, tomamos como referencia los valores (serie temporal) para el valor intermedio entre las velocidades (6.69 m/s.)
u10_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(u10_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot U-component wind 10 meters (points) vs. fuel consumption",color = "points") +
xlab("U-component wind 10 meters [m/s]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Dispersión, no se visualiza recta posible
v10_values_pts <- as.data.frame(v10.wind_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(v10_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot V-component wind 10 meters (points)",fill = "points") +
xlab("points") +
ylab("value (m/s)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar semejanzas, diferencias.
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(v10.wind_pts_ts)
## $`1`
## out_dates out_values
## 1 2000-10-01 -5.239399
##
## $`2`
## out_dates out_values
## 1 2001-11-01 -3.205898
## 2 2017-04-01 -2.697417
##
## $`3`
## data frame with 0 columns and 0 rows
##
## $`4`
## out_dates out_values
## 1 1993-01-01 4.597113
## 2 2002-01-01 5.492106
## 3 2012-04-01 -6.477585
## 4 2015-12-01 8.423544
Contrastar con periodos.
descrp_v10_ts <- ts(data = data.frame(pnt_1=v10.wind_pts_ts$`1`,pnt_2=v10.wind_pts_ts$`2`,pnt_3=v10.wind_pts_ts$`3`,pnt_4=v10.wind_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_v10_ts, title = "v10-component wind (points)", type = "multiple")
Como era de esperar al ser distintos puntos geográficos los trazados de las series temporales no guardan similitud.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_v10.pt1 <- spec.pgram(v10.wind_pts_ts$`1`,taper=0,log='no')
spec_v10.pt2 <- spec.pgram(v10.wind_pts_ts$`2`,taper=0,log='no')
spec_v10.pt3 <- spec.pgram(v10.wind_pts_ts$`3`,taper=0,log='no')
spec_v10.pt4 <- spec.pgram(v10.wind_pts_ts$`4`, taper=0,log='no')
En este caso la componente a frecuencia 2 solo destaca en el punto 2 y algo menos en los 1 y 3
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(v10.wind_pts_ts$`1`, color = "Reds", title = "v10-component wind (pt. 1)")
ts_heatmap(v10.wind_pts_ts$`3`, color = "Reds", title = "v10-component wind (pt. 3)")
En el punto 1 con espectrograma mas plano si se observa cierta uniformidad anual, con valores altos positivos en Julio y extremos negativos en Noviembre y Diciembre.
En el segundo de espectrograma más complejo vemos como se pierde esta uniformidad.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_v10_trends <- ts(data = data.frame(pnt_1=v10.wind_pts_trend$`1`,pnt_2=v10.wind_pts_trend$`2`,pnt_3=v10.wind_pts_trend$`3`,pnt_4=v10.wind_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_v10_trends, title = "v10-component wind and fuel consumption trends (points)", type = "multiple")
NOTA: COMPARAR CON LOS PERIODOS DE VALORES ALTOS Y DE MÁXIMA FLUCTUACIÓN, POR SI HAY ALGUNA RELACIÓN Parece q no se observa relación
v10_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(v10_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot V-component wind 10 meters (points) vs. fuel consumption",color = "points") +
xlab("V-component wind 10 meters [m/s]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
En los 2 primeros puntos un mayor agrupamiento, dada la ausencia en los otros 2 no parece significativo.
mwd_values_pts <- as.data.frame(m.wave.dir_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(mwd_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot Mean wave direction (points)",fill = "points") +
xlab("points") +
ylab("degrees relative North (clockwise)") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
NOTA: hablar de la concentración y diferencias
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(m.wave.dir_pts_ts)
## $`1`
## out_dates out_values
## 1 1993-02-01 353.3390
## 2 1993-12-01 353.8554
## 3 1995-01-01 335.7270
## 4 1995-02-01 341.3194
## 5 1996-02-01 329.0360
## 6 1997-12-01 321.0870
## 7 2001-01-01 342.0445
## 8 2002-01-01 358.2172
## 9 2002-12-01 338.7484
## 10 2003-01-01 321.3067
## 11 2004-01-01 335.1832
## 12 2005-03-01 316.5549
## 13 2006-02-01 350.1748
## 14 2006-03-01 359.9312
## 15 2007-02-01 312.9731
## 16 2009-01-01 358.0030
## 17 2010-01-01 331.0466
## 18 2010-02-01 322.5153
## 19 2010-03-01 346.6260
## 20 2010-12-01 343.9782
## 21 2011-01-01 329.0525
## 22 2013-02-01 353.4379
## 23 2013-03-01 333.4417
## 24 2014-01-01 326.5420
## 25 2017-01-01 346.4228
##
## $`2`
## data frame with 0 columns and 0 rows
##
## $`3`
## out_dates out_values
## 1 1999-02-01 219.600887
## 2 1999-11-01 7.641440
## 3 2000-03-01 202.741475
## 4 2001-11-01 131.991275
## 5 2003-10-01 20.490630
## 6 2004-10-01 12.096632
## 7 2005-02-01 159.486015
## 8 2005-11-01 5.235307
## 9 2006-08-01 165.440920
## 10 2007-11-01 22.655051
## 11 2008-09-01 3.823489
## 12 2008-11-01 70.541494
## 13 2010-11-01 6.487815
## 14 2010-12-01 9.047765
## 15 2011-05-01 21.012508
## 16 2011-06-01 222.622287
## 17 2012-09-01 16.062906
## 18 2013-05-01 37.350042
## 19 2014-12-01 75.853664
## 20 2017-04-01 27.115736
## 21 2017-11-01 11.613208
##
## $`4`
## out_dates out_values
## 1 1993-02-01 5.0210624
## 2 1993-10-01 15.4805997
## 3 1995-06-01 6.6086707
## 4 1997-04-01 23.4406152
## 5 2001-12-01 24.1602577
## 6 2002-09-01 0.6592595
## 7 2005-02-01 2.6259162
## 8 2005-03-01 238.2182044
## 9 2007-09-01 28.6484100
## 10 2007-11-01 24.7755246
## 11 2009-09-01 356.5966547
## 12 2010-12-01 50.1717646
## 13 2017-04-01 18.6613098
Repasar fechas, Contrastar con periodos
descrp_mwd_ts <- ts(data = data.frame(pnt_1=m.wave.dir_pts_ts$`1`,pnt_2=m.wave.dir_pts_ts$`2`,pnt_3=m.wave.dir_pts_ts$`3`,pnt_4=m.wave.dir_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_mwd_ts, title = "Mean wave direction (points)", type = "multiple")
Ptos 2 y 3 trazado similar (sin correspondecia temporal) se detecta concentración alrededor de 300 y outliers (boxplots)
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_mwd.pt1 <- spec.pgram(m.wave.dir_pts_ts$`1`,taper=0,log='no')
spec_mwd.pt2 <- spec.pgram(m.wave.dir_pts_ts$`2`,taper=0,log='no')
spec_mwd.pt3 <- spec.pgram(m.wave.dir_pts_ts$`3`,taper=0,log='no')
spec_mwd.pt4 <- spec.pgram(m.wave.dir_pts_ts$`4`,taper=0,log='no')
Espectros complejos puntos 3 y 4. Planos en el uno y 2, con la sorpresa para el primero de frecuencia 2 dominante.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(m.wave.dir_pts_ts$`1`, color = "Reds", title = "Mean wave direcction (pt. 1)")
ts_heatmap(m.wave.dir_pts_ts$`3`, color = "Reds", title = "Mean wave direcction (pt. 3)")
Como habiamos visto en el punto 1 se puede observar cierto patrón anual, con valores medios en Julio, bajos en Octubre y de forma menos uniforme extremos en Enero y Para el punto 3 con espectro mucho más complejo no es posible.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_mwd_trends <- ts(data = data.frame(pnt_1=m.wave.dir_pts_trend$`1`,pnt_2=m.wave.dir_pts_trend$`2`,pnt_3=m.wave.dir_pts_trend$`3`,pnt_4=m.wave.dir_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_mwd_trends, title = "v10-component wind and fuel consumption trends (points)", type = "multiple")
Contrastar con periodos de tiempo. No parece haber relación.
mwd_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(mwd_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot Mean wave direction (points) vs. fuel consumption",color = "points") +
xlab("Mean wave direction [degrees relative North (clockwise)]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
INterpretar direccion respecto al gasto combustible, comentar agrupacion (anteriores conclusiones no generales)
mwp_values_pts <- as.data.frame(m.wave.period_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(mwp_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot Mean wave period (points)",fill = "points") +
xlab("points") +
ylab("period [s.]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar valores y diferencias
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(m.wave.period_pts_ts)
## $`1`
## out_dates out_values
## 1 1995-09-01 9.024435
## 2 2003-09-01 8.441276
## 3 2009-03-01 8.426676
## 4 2017-09-01 9.265599
##
## $`2`
## data frame with 0 columns and 0 rows
##
## $`3`
## data frame with 0 columns and 0 rows
##
## $`4`
## data frame with 0 columns and 0 rows
Pocos outliers y sin relacion (tiempos separados)
descrp_mwp_ts <- ts(data = data.frame(pnt_1=m.wave.period_pts_ts$`1`,pnt_2=m.wave.period_pts_ts$`2`,pnt_3=m.wave.period_pts_ts$`3`,pnt_4=m.wave.period_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_mwp_ts, title = "Mean wave period (points)", type = "multiple")
Trazados similares para los puntos del 2 al 4,observandose cierta periodicidad.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_mwp.pt1 <- spec.pgram(m.wave.period_pts_ts$`1`,taper=0,log='no')
spec_mwp.pt2 <- spec.pgram(m.wave.period_pts_ts$`2`,taper=0,log='no')
spec_mwp.pt3 <- spec.pgram(m.wave.period_pts_ts$`3`,taper=0,log='no')
spec_mwp.pt4 <- spec.pgram(m.wave.period_pts_ts$`4`,taper=0,log='no')
Predominio claro en todas de la frecuencia 1. Como ya se había observado en el trazado (periodicidad clara), espectros planos en los puntos 2 al 4 exceptuando la frecuencia 1.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(m.wave.period_pts_ts$`1`, color = "Reds", title = "Mean wave period (pt. 1)")
ts_heatmap(m.wave.period_pts_ts$`3`, color = "Reds", title = "Mean wave period (pt. 3)")
En el punto 3 con espectro de frecuencia predominante observamos claro patrón anual, con extremos bajos en Julio y los altos en Diciembre y Enero.
Para el punto 1 de espectro algo más complejo el patrón no es tan claro
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_mwp_trends <- ts(data = data.frame(pnt_1=m.wave.period_pts_trend$`1`,pnt_2=m.wave.period_pts_trend$`2`,pnt_3=m.wave.period_pts_trend$`3`,pnt_4=m.wave.period_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_mwp_trends, title = "Mean wave period and fuel consumption trends (points)", type = "multiple")
Contrastar con periodos. Quizas patrón para periodo altos consumos.
mwp_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(mwp_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot Mean wave period (points) vs. fuel consumption",color = "points") +
xlab("Mean wave period [s.]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Aunque agrupacion a menos, parece cierta relacion lineal
swh_values_pts <- as.data.frame(sig.wave.height_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(swh_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot Significant wave height (points)",fill = "points") +
xlab("points") +
ylab("signf. wave height [m.]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar valores, diferencias
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(sig.wave.height_pts_ts)
## $`1`
## data frame with 0 columns and 0 rows
##
## $`2`
## data frame with 0 columns and 0 rows
##
## $`3`
## data frame with 0 columns and 0 rows
##
## $`4`
## out_dates out_values
## 1 2014-02-01 5.970439
Ausencia outliers
descrp_swh_ts <- ts(data = data.frame(pnt_1=sig.wave.height_pts_ts$`1`,pnt_2=sig.wave.height_pts_ts$`2`,pnt_3=sig.wave.height_pts_ts$`3`,pnt_4=sig.wave.height_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_swh_ts, title = "Significant wave height (points)", type = "multiple")
Trazado con cierta similitud en los puntos 2 al 4, observandose señales de periodicidad.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_swh.pt1 <- spec.pgram(sig.wave.height_pts_ts$`1`,taper=0,log='no')
spec_swh.pt2 <- spec.pgram(sig.wave.height_pts_ts$`2`,taper=0,log='no')
spec_swh.pt3 <- spec.pgram(sig.wave.height_pts_ts$`3`,taper=0,log='no')
spec_swh.pt4 <- spec.pgram(sig.wave.height_pts_ts$`4`,taper=0,log='no')
Clara frecuencia dominante anual con resto plano, confirmando la periodicidad.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(sig.wave.height_pts_ts$`1`, color = "Reds", title = "Significant wave height (pt. 1)")
ts_heatmap(sig.wave.height_pts_ts$`3`, color = "Reds", title = "Significant wave height (pt. 3)")
En ambos se observa patrón anual, con extremos bajos en Julio y altos en Enero
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_swh_trends <- ts(data = data.frame(pnt_1=sig.wave.height_pts_trend$`1`,pnt_2=sig.wave.height_pts_trend$`2`,pnt_3=sig.wave.height_pts_trend$`3`,pnt_4=sig.wave.height_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_swh_trends, title = "Significant wave height and fuel consumption trends (points)", type = "multiple")
Periodo altos consumos, ptos 3 y 4, algo menos el 2. Priodo fluctuación solo pts 3 y 4
swh_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(swh_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot Significant wave height (points) vs. fuel consumption",color = "points") +
xlab("signf. wave height [m.]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Con agrupación a menos, parece cierta relacion lineal
bfi_values_pts <- as.data.frame(bfi_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(bfi_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Boxplot BFI (points)",fill = "points") +
xlab("points") +
ylab("BFI [dimensionless]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
SEÑALAR VALORES BAJOS Y POCA VARIANZA, que ya se veian en el mapa de enero de 1993
No vale la pena analizar outliers
descrp_bfi_ts <- ts(data = data.frame(pnt_1=bfi_pts_ts$`1`,pnt_2=bfi_pts_ts$`2`,pnt_3=bfi_pts_ts$`3`,pnt_4=bfi_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_bfi_ts, title = "BFI (points)", type = "multiple")
Dados los valores tan bajos (casi ruido) y su uniformidad (patente mapa) pasamos a examinar directamente la tendencia.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_bfi_trends <- ts(data = data.frame(pnt_1=bfi_pts_trend$`1`,pnt_2=bfi_pts_trend$`2`,pnt_3=bfi_pts_trend$`3`,pnt_4=bfi_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_bfi_trends, title = "BFI trend (points)", type = "multiple")
No se observa relación con periodos (no ayuda el infimo rango de valores de la variable)
bfi_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(bfi_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "BFI (points) vs. fuel consumption",color = "points") +
xlab("BFI [dimensionless]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar no se pueden sacar conclusiones.
pwp_values_pts <- as.data.frame(peak.wave.period_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(pwp_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Peak wave period (points)",fill = "points") +
xlab("points") +
ylab("Peak wave period [s.]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar valores y diferencias
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(peak.wave.period_pts_ts)
## $`1`
## out_dates out_values
## 1 1995-08-01 10.524878
## 2 1995-09-01 10.902210
## 3 1996-12-01 10.229273
## 4 1999-09-01 10.418808
## 5 2000-07-01 6.509428
## 6 2003-09-01 10.256442
## 7 2009-03-01 10.327518
## 8 2013-07-01 6.803511
## 9 2017-09-01 11.145867
##
## $`2`
## data frame with 0 columns and 0 rows
##
## $`3`
## data frame with 0 columns and 0 rows
##
## $`4`
## data frame with 0 columns and 0 rows
Solo en el primer punto, agosto y septiembre 1995 (golfo mexico). Contrastar con periodos
descrp_pwp_ts <- ts(data = data.frame(pnt_1=peak.wave.period_pts_ts$`1`,pnt_2=peak.wave.period_pts_ts$`2`,pnt_3=peak.wave.period_pts_ts$`3`,pnt_4=peak.wave.period_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_pwp_ts, title = "Peak wave period (points)", type = "multiple")
Se observa cierta semejanza en el trazado de los puntos 1 al 4. En los mismos también parec verse señales de cierta periodicidad.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_pwp.pt1 <- spec.pgram(peak.wave.period_pts_ts$`1`,taper=0,log='no')
spec_pwp.pt2 <- spec.pgram(peak.wave.period_pts_ts$`2`,taper=0,log='no')
spec_pwp.pt3 <- spec.pgram(peak.wave.period_pts_ts$`3`,taper=0,log='no')
spec_pwp.pt4 <- spec.pgram(peak.wave.period_pts_ts$`4`,taper=0,log='no')
Se comprueba periodicidad con frecuencia anual y resto espectro plano en los puntos 2 al 4. En el punto 1 predomina frecuencia anual con espectro más complejo.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(peak.wave.period_pts_ts$`1`, color = "Reds", title = "Peak wave period (pt. 1)")
ts_heatmap(peak.wave.period_pts_ts$`3`, color = "Reds", title = "Peak wave period (pt. 3)")
Para el punto 3 se comprueba periodicidad anual con valores extremos altos en diciembre y enero, y los bajos en Julio. En el pto 1 de espectro más complejo no queda claro.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_pwp_trends <- ts(data = data.frame(pnt_1=peak.wave.period_pts_trend$`1`,pnt_2=peak.wave.period_pts_trend$`2`,pnt_3=peak.wave.period_pts_trend$`3`,pnt_4=peak.wave.period_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_pwp_trends, title = "Peak wave period and fuel consumption trends (points)", type = "multiple")
Contrastar con periodos. Algo de coincidencia para el máximo
pwp_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(pwp_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Peak wave period (points) vs. fuel consumption",color = "points") +
xlab("Peak wave period [s.]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Concentracion a menos, parece cierta relacion lineal
wsk_values_pts <- as.data.frame(wave.spectr.kurt_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(wsk_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Wave spectral kurtosis (points)",fill = "points") +
xlab("points") +
ylab("Wave spectral kurtosis [dimensionless]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Al igual que BFI, valores muy bajos y poca varianza. Recordar mapa de un solo color No vale la pena analizar outliers.
En cuanto al análisis temporal, al igual que con la variable BFI los valores muy bajos muy baja varianza hacen que la series temporales sean practicamente ruido, sin influencia clara.
wsk_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(wsk_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Wave spectral kurtosis (points) vs. fuel consumption",color = "points") +
xlab("Wave spectral kurtosis [dimensionless]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Cortos valores y rangos, ptos muy dispersos.
miwh_values_pts <- as.data.frame(max.indiv.wave.height_pts_ts) %>%
mutate(year=as.character(floor(time(u10.wind_pts_ts$`1`)))) %>%
mutate(month=as.character(cycle(u10.wind_pts_ts$`1`))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
ggplot(miwh_values_pts, aes(x = variable, y = value)) +
geom_boxplot(aes(fill = variable)) +
scale_fill_discrete() +
labs(title = "Maximum individual wave height (points)",fill = "points") +
xlab("points") +
ylab("Max. individual wave height [m.]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Comentar valores y diferencias
Veamos a que fechas se corresponden los outliers:
outliers_date_ts(max.indiv.wave.height_pts_ts)
## $`1`
## data frame with 0 columns and 0 rows
##
## $`2`
## data frame with 0 columns and 0 rows
##
## $`3`
## out_dates out_values
## 1 2000-12-01 10.28404
##
## $`4`
## out_dates out_values
## 1 2014-02-01 11.09553
Pocos outliers
descrp_miwh_ts <- ts(data = data.frame(pnt_1=max.indiv.wave.height_pts_ts$`1`,pnt_2=max.indiv.wave.height_pts_ts$`2`,pnt_3=max.indiv.wave.height_pts_ts$`3`,pnt_4=max.indiv.wave.height_pts_ts$`4`), start = 1993, frequency = 12)
ts_plot(descrp_miwh_ts, title = "Max. Individual wave height (points)", type = "multiple")
De nuevo similitudes en el trazado en los puntos 2 al 4, y en mnor medida pero también para el primero. Se vislumbra la existencia de periodicidad en los primeros puntos nombrados.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_miwh.pt1 <- spec.pgram(max.indiv.wave.height_pts_ts$`1`,taper=0,log='no')
spec_miwh.pt2 <- spec.pgram(max.indiv.wave.height_pts_ts$`2`,taper=0,log='no')
spec_miwh.pt3 <- spec.pgram(max.indiv.wave.height_pts_ts$`3`,taper=0,log='no')
spec_miwh.pt4 <- spec.pgram(max.indiv.wave.height_pts_ts$`4`,taper=0,log='no')
Clara periodicidad anual en todos los puntos, con resto del espectro plano.
Vamos a observar el mapa de calor para los puntos 1 y 3, que presentan la mayor diferencia:
ts_heatmap(max.indiv.wave.height_pts_ts$`1`, color = "Reds", title = "Max. Individual wave height (points) (pt. 1)")
ts_heatmap(max.indiv.wave.height_pts_ts$`3`, color = "Reds", title = "Max. Individual wave height (points) (pt. 3)")
Patrón anual claro con extremos ionferiores en julio y los superiores en enero.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_miwh_trends <- ts(data = data.frame(pnt_1=max.indiv.wave.height_pts_trend$`1`,pnt_2=max.indiv.wave.height_pts_trend$`2`,pnt_3=max.indiv.wave.height_pts_trend$`3`,pnt_4=max.indiv.wave.height_pts_trend$`4`,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_miwh_trends, title = "Max. Individual wave height and fuel consumption trends (points)", type = "multiple")
Cierta correspondencia en el periodo de consumos altos y en el máximo (ptos 2 al 4 y 3 y 4 respectivamente).
miwh_values_pts$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(miwh_values_pts, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Maximum individual wave height (points) vs. fuel consumption",color = "points") +
xlab("Max. individual wave height [m.]") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Concentracion a menos, parece cierta relación lineal.
Para este punto necesitamos tener los datos por punto, un dataframe para cada punto con el valor de todas las variables. Eliminamos las variables “Benjamin Feir Index (BFI)” y “Wave spectral kurtosis” a raíz de los resultados del punto anterior en el que comprobamos que tienen varianza cercana a cero.
Ptrimero inicializamos las variables necesarias para el proceso
n_points <- 4
n_data <- 300
vars_mesoc_ts <- list(u10.wind_pts_ts,v10.wind_pts_ts,m.wave.dir_pts_ts,m.wave.period_pts_ts,sig.wave.height_pts_ts,peak.wave.period_pts_ts,max.indiv.wave.height_pts_ts)
names_vars_mesoc_ts <- c("u10_wind","v10_wind","mean_wave_direct","mean_wave_period","signf_wave_height","peak_wave_period","max_indiv_wv_height")
df_pt1 <- data.frame(idx_time=1:n_data)
df_pt2 <- data.frame(idx_time=1:n_data)
df_pt3 <- data.frame(idx_time=1:n_data)
df_pt4 <- data.frame(idx_time=1:n_data)
df_points_list <- list(df_pt1,df_pt2,df_pt3,df_pt4)
Finalmente recorremos cada variable (lista con los valores para los 4 puntos) y vamos guardando los valores de cada punto en su dataframe correspondiente:
for (nvar in 1:length(names_vars_mesoc_ts)) {
m_name <- names_vars_mesoc_ts[nvar]
m_var <- vars_mesoc_ts[[nvar]]
for (npoint in 1:n_points) {
df_points_list[[npoint]][m_name] <- m_var[[npoint]]
}
}
str(df_points_list)
## List of 4
## $ :'data.frame': 300 obs. of 8 variables:
## ..$ idx_time : int [1:300] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ u10_wind : Time-Series [1:300] from 1993 to 2018: -1.68 2.25 1.13 1.01 -1.54 ...
## ..$ v10_wind : Time-Series [1:300] from 1993 to 2018: 1.454 -0.638 0.88 -0.572 0.792 ...
## ..$ mean_wave_direct : Time-Series [1:300] from 1993 to 2018: 86.1 353.3 17.1 27.6 91 ...
## ..$ mean_wave_period : Time-Series [1:300] from 1993 to 2018: 7.54 7.41 7.56 7.21 7.39 ...
## ..$ signf_wave_height : Time-Series [1:300] from 1993 to 2018: 2.2 2.25 2.41 1.95 1.29 ...
## ..$ peak_wave_period : Time-Series [1:300] from 1993 to 2018: 9.03 8.59 8.92 8.52 9.06 ...
## ..$ max_indiv_wv_height: Time-Series [1:300] from 1993 to 2018: 4.17 4.28 4.57 3.71 2.44 ...
## $ :'data.frame': 300 obs. of 8 variables:
## ..$ idx_time : int [1:300] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ u10_wind : Time-Series [1:300] from 1993 to 2018: 4.7748 1.7727 2.4426 0.0225 0.4191 ...
## ..$ v10_wind : Time-Series [1:300] from 1993 to 2018: 0.696 3.928 2.594 1.848 2.548 ...
## ..$ mean_wave_direct : Time-Series [1:300] from 1993 to 2018: 296.6 274.5 255.5 10.2 36.1 ...
## ..$ mean_wave_period : Time-Series [1:300] from 1993 to 2018: 8.39 8.2 8.18 7.96 7.9 ...
## ..$ signf_wave_height : Time-Series [1:300] from 1993 to 2018: 3.05 2.9 2.9 2.46 2.12 ...
## ..$ peak_wave_period : Time-Series [1:300] from 1993 to 2018: 9.75 9.72 9.46 9.32 9.27 ...
## ..$ max_indiv_wv_height: Time-Series [1:300] from 1993 to 2018: 5.74 5.45 5.46 4.66 4.01 ...
## $ :'data.frame': 300 obs. of 8 variables:
## ..$ idx_time : int [1:300] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ u10_wind : Time-Series [1:300] from 1993 to 2018: 4.74 -2.03 2.95 1.17 1.53 ...
## ..$ v10_wind : Time-Series [1:300] from 1993 to 2018: 1.304 0.549 -1.312 1.323 -2.442 ...
## ..$ mean_wave_direct : Time-Series [1:300] from 1993 to 2018: 292 322 328 312 357 ...
## ..$ mean_wave_period : Time-Series [1:300] from 1993 to 2018: 9.8 8.66 9.56 8.82 8.52 ...
## ..$ signf_wave_height : Time-Series [1:300] from 1993 to 2018: 3.81 2.85 3.53 2.7 2.51 ...
## ..$ peak_wave_period : Time-Series [1:300] from 1993 to 2018: 11.6 10.5 11.4 10.8 10.3 ...
## ..$ max_indiv_wv_height: Time-Series [1:300] from 1993 to 2018: 7.09 5.35 6.6 5.05 4.71 ...
## $ :'data.frame': 300 obs. of 8 variables:
## ..$ idx_time : int [1:300] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ u10_wind : Time-Series [1:300] from 1993 to 2018: 4.3882 -5.2959 -0.9756 4.8805 -0.0784 ...
## ..$ v10_wind : Time-Series [1:300] from 1993 to 2018: 4.597 -3.183 1 -2.79 -0.637 ...
## ..$ mean_wave_direct : Time-Series [1:300] from 1993 to 2018: 279.4 5.02 302.83 304.78 329.06 ...
## ..$ mean_wave_period : Time-Series [1:300] from 1993 to 2018: 10.61 9.07 10 8.94 7.93 ...
## ..$ signf_wave_height : Time-Series [1:300] from 1993 to 2018: 4.09 2.64 3.32 3.22 2.31 ...
## ..$ peak_wave_period : Time-Series [1:300] from 1993 to 2018: 12.87 11.19 11.87 10.92 9.69 ...
## ..$ max_indiv_wv_height: Time-Series [1:300] from 1993 to 2018: 7.6 4.96 6.18 6.05 4.36 ...
Comprobamos que se ha realizado bien la carga de los dataframe de cada punto con una de las variables:
str(u10.wind_pts_ts)
## List of 4
## $ 1: Time-Series [1:300] from 1993 to 2018: -1.68 2.25 1.13 1.01 -1.54 ...
## $ 2: Time-Series [1:300] from 1993 to 2018: 4.7748 1.7727 2.4426 0.0225 0.4191 ...
## $ 3: Time-Series [1:300] from 1993 to 2018: 4.74 -2.03 2.95 1.17 1.53 ...
## $ 4: Time-Series [1:300] from 1993 to 2018: 4.3882 -5.2959 -0.9756 4.8805 -0.0784 ...
Vamos a calcular la matriz de correlación para los dataframes con las variables de cada punto, a la que añadimos como nueva variable el consumo de fuel para la velocidad que anteriormente hemos escogido de referencia (6.69 m/s).
df1 <- df_points_list[[1]] %>%
select(-idx_time) %>%
mutate(fuel_consump=fuel_cons_speed_ts$`6.69`)
mtrx_cor1 <- cor(df1)
mtrx_cor1
## u10_wind v10_wind mean_wave_direct
## u10_wind 1.00000000 0.1933377 0.49149006
## v10_wind 0.19333766 1.0000000 0.19249197
## mean_wave_direct 0.49149006 0.1924920 1.00000000
## mean_wave_period -0.10286976 -0.5649742 -0.14125309
## signf_wave_height 0.09154718 -0.6461760 0.01501317
## peak_wave_period -0.14620597 -0.4540390 -0.16690738
## max_indiv_wv_height 0.10013025 -0.6419378 0.02131613
## fuel_consump -0.31812031 -0.6433532 -0.26081642
## mean_wave_period signf_wave_height peak_wave_period
## u10_wind -0.1028698 0.09154718 -0.1462060
## v10_wind -0.5649742 -0.64617600 -0.4540390
## mean_wave_direct -0.1412531 0.01501317 -0.1669074
## mean_wave_period 1.0000000 0.71428655 0.9269852
## signf_wave_height 0.7142865 1.00000000 0.5402925
## peak_wave_period 0.9269852 0.54029251 1.0000000
## max_indiv_wv_height 0.7026276 0.99980506 0.5276190
## fuel_consump 0.5313981 0.52349202 0.4642619
## max_indiv_wv_height fuel_consump
## u10_wind 0.10013025 -0.3181203
## v10_wind -0.64193783 -0.6433532
## mean_wave_direct 0.02131613 -0.2608164
## mean_wave_period 0.70262762 0.5313981
## signf_wave_height 0.99980506 0.5234920
## peak_wave_period 0.52761901 0.4642619
## max_indiv_wv_height 1.00000000 0.5184317
## fuel_consump 0.51843170 1.0000000
Podremos sacar más facilmente conclusiones con la gráfica:
library(corrplot)
## Warning: package 'corrplot' was built under R version 3.6.2
corrplot(mtrx_cor1, method = "shade", shade.col = NA, addCoef.col = TRUE, tl.col = "black", tl.srt = 45)
En primer lugar observamos una muy alta correlación (0.93) entre las variables “peak_wave_period” y “mean_”wave_period“. Y con total rotundidad entre las dos relativas de altura”signf_wave_height" y “max_indiv_wv_height”, que llega a la unidad. Estos nos llevaría a prescindir de alguna en cada par a la hora de aplicar PCA. Dejamos la elección a después de ver los resultados en los puntos restantes.
En cuanto al consumo de combustible, sin llegar a ser altas vemos correlacion negativa para “la componente vertical (hacia el norte) del viento”v10_wind" y positivas por orden decreciente para “mean_wave_period”, “signif_wave_height” y “max_indiv_wv_height”.
df2 <- df_points_list[[2]] %>%
select(-idx_time) %>%
mutate(fuel_consump=fuel_cons_speed_ts$`6.69`)
mtrx_cor2 <- cor(df2)
corrplot(mtrx_cor2, method = "shade", shade.col = NA, addCoef.col = TRUE, tl.col = "black", tl.srt = 45)
corr entre variables: ademas de las nombradas se suman las de altura con periodicidad de las olas.
corr con fuel: las mismas anteriores pero con ligero descenso, más acuciado en las de altura
df3 <- df_points_list[[3]] %>%
select(-idx_time) %>%
mutate(fuel_consump=fuel_cons_speed_ts$`6.69`)
mtrx_cor3 <- cor(df3)
corrplot(mtrx_cor3, method = "shade", shade.col = NA, addCoef.col = TRUE, tl.col = "black", tl.srt = 45)
corr entre variables: las mismas, más alta todavía entre altura y periodo de las olas.
corr con fuel: desaparece como a considerar la componente de viento. Las restantes mas parecidas al primer punto.
df4 <- df_points_list[[4]] %>%
select(-idx_time) %>%
mutate(fuel_consump=fuel_cons_speed_ts$`6.69`)
mtrx_cor4 <- cor(df4)
corrplot(mtrx_cor4, method = "shade", shade.col = NA, addCoef.col = TRUE, tl.col = "black", tl.srt = 45)
corr entre variables: IGUAL PUNTO ANTERIOR
corr con fuel: desaparece como a considerar la componente de viento. Sin grandes cambio resto.
A cada uno de los dataframes de cada punto le aplicamos PCA. Eliminamos antes las variables “signf_wave_height” y “peak_wave_period”, quedándonos con “max_indiv_wv_height” y “mean_wave_period”, por su muy alta correlación.
df1pca <- df1 %>%
select(-fuel_consump,-signf_wave_height,-peak_wave_period)
pca1 <- prcomp(df1pca)
summary(pca1)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 78.717 1.7546 1.60127 0.58477 0.30790
## Proportion of Variance 0.999 0.0005 0.00041 0.00006 0.00002
## Cumulative Proportion 0.999 0.9995 0.99993 0.99998 1.00000
Observamos que ya con la primera componente conservamos prácticamente la totalidad de la varianza (99.9%)
reducc_pca1_ts <- ts(pca1$x[,1], start = 1993, frequency=12)
reducc_pca1_ts.dc <- stl(reducc_pca1_ts, s.window = "periodic", na.action = na.omit)
reducc_pca1_trend <- reducc_pca1_ts.dc$time.series[,"trend"]
plot(reducc_pca1_ts.dc)
df2pca <- df2 %>%
select(-fuel_consump,-signf_wave_height,-peak_wave_period)
pca2 <- prcomp(df2pca)
summary(pca2)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 109.2403 2.17168 1.68889 0.97775 0.27244
## Proportion of Variance 0.9993 0.00039 0.00024 0.00008 0.00001
## Cumulative Proportion 0.9993 0.99967 0.99991 0.99999 1.00000
reducc_pca2_ts <- ts(pca2$x[,1], start = 1993, frequency=12)
reducc_pca2_ts.dc <- stl(reducc_pca2_ts, s.window = "periodic", na.action = na.omit)
reducc_pca2_trend <- reducc_pca2_ts.dc$time.series[,"trend"]
plot(reducc_pca2_ts.dc)
df3pca <- df3 %>%
select(-fuel_consump,-signf_wave_height,-peak_wave_period)
pca3 <- prcomp(df3pca)
summary(pca3)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 66.7888 2.5908 1.83202 1.47257 0.29412
## Proportion of Variance 0.9972 0.0015 0.00075 0.00048 0.00002
## Cumulative Proportion 0.9972 0.9988 0.99950 0.99998 1.00000
reducc_pca3_ts <- ts(pca3$x[,1], start = 1993, frequency=12)
reducc_pca3_ts.dc <- stl(reducc_pca3_ts, s.window = "periodic", na.action = na.omit)
reducc_pca3_trend <- reducc_pca3_ts.dc$time.series[,"trend"]
plot(reducc_pca3_ts.dc)
df4pca <- df4 %>%
select(-fuel_consump,-signf_wave_height,-peak_wave_period)
pca4 <- prcomp(df4pca)
summary(pca4)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 57.1322 3.07180 1.87621 1.44639 0.36251
## Proportion of Variance 0.9954 0.00288 0.00107 0.00064 0.00004
## Cumulative Proportion 0.9954 0.99825 0.99932 0.99996 1.00000
reducc_pca4_ts <- ts(pca4$x[,1], start = 1993, frequency=12)
reducc_pca4_ts.dc <- stl(reducc_pca4_ts, s.window = "periodic", na.action = na.omit)
reducc_pca4_trend <- reducc_pca4_ts.dc$time.series[,"trend"]
plot(reducc_pca4_ts.dc)
pca_vs_fuel <- data.frame(pca_1=reducc_pca1_ts,pca_2=reducc_pca2_ts,pca_3=reducc_pca3_ts,pca_4=reducc_pca4_ts) %>%
mutate(year=as.character(floor(time(reducc_pca1_ts)))) %>%
mutate(month=as.character(cycle(reducc_pca1_ts))) %>%
mutate(day="01") %>%
unite(date,year,month,day,sep = "-") %>%
mutate(date=as.Date(date)) %>%
select(date,everything()) %>%
gather(key = "variable", value = "value", -date)
pca_vs_fuel$fuel_ref <- fuel_cons_speed_ts$`6.69`
ggplot(pca_vs_fuel, aes(x = value,group = variable,color=variable)) +
geom_point(aes(y=fuel_ref)) +
facet_wrap( ~ variable, ncol = 2) +
scale_color_discrete() +
labs(title = "Scatter plot Reducc. PCA (points) vs. fuel consumption",color = "points") +
xlab("Reducc. PCA") +
ylab("fuel consumption (speed = 6.69 m/s) [kg or m^3]") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5))
Exceptuando en el punto 2, donde los puntos de la gráfica están mas dispersos, se observa cierta relación lineal con pendiente negativa, llegando a ser casi vertical en el punto 4.
descrp_pca_ts <- ts(data = data.frame(pnt_1=reducc_pca1_ts,pnt_2=reducc_pca2_ts,pnt_3=reducc_pca3_ts,pnt_4=reducc_pca4_ts), start = 1993, frequency = 12)
ts_plot(descrp_miwh_ts, title = "PCA (points)", type = "multiple")
Se observa alguna semejanza en el trazado para los 4 puntos y la posible existencia de periodicidad menos clara en el primero.
En cuanto a la estacionalidad pasamos a examinar el periodograma de las tres:
par(mfrow=c(4,1))
spec_pca.pt1 <- spec.pgram(reducc_pca1_ts,taper=0,log='no')
spec_pca.pt2 <- spec.pgram(reducc_pca2_ts,taper=0,log='no')
spec_pca.pt3 <- spec.pgram(reducc_pca3_ts, taper=0,log='no')
spec_pca.pt4 <- spec.pgram(reducc_pca4_ts, taper=0,log='no')
Espectro con periodicidad predominante en los dos primeros, con la sorpresa de ser para la frecuencia2 el primero. Espectros mucho más complejos en los otros dos puntos con lo cual no hay componente de frecuencia clara.
Vamos a hacer el mapa de calor en este caso para los puntos 2 y 3 (más dispares y el segundo en principio con frecuencia anual)
ts_heatmap(reducc_pca2_ts, color = "Reds", title = "PCA (pt. 2)")
ts_heatmap(reducc_pca3_ts, color = "Reds", title = "PCA (pt. 3)")
En el punto 2 se observa uniformidad en los valores para los meses de Julio y Enero, y presencia mayoritaria de valores extremos en Septiembre, que señalarían la frecuencia anual vista en el spectrograma
No se puede detectar un claro patrón en el punto 3, con predominio de valores bajos para casi todos los meses.
Vamos a contrastar las tendencias entre los puntos y con el consumo de fuel (tomamos como valor de velocidad el intermedio)
descrp_pca_trends <- ts(data = data.frame(pnt_1=reducc_pca1_trend,pnt_2=reducc_pca2_trend,pnt_3=reducc_pca3_trend,pnt_4=reducc_pca4_trend,fuel_consumpt=fuel_cons_speed_trends$`6.69`), start = 1993, frequency = 12)
ts_plot(descrp_pca_trends, title = "PCA (points) and fuel consumption trends ", type = "multiple")
Solo se observa correspondencia alguna correspondencia con el máximo en los 3 primeos puntos